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Adam M. Novak
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Kamshat Temirbayeva
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Slađan Rašić
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Tim Dudgeon
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Viktoria Isabel Schwarz
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Christoph Steinbeck
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Nina Van Goethem
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Maria Bouga
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Konstantinos Kyritsis
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Bruno P. Kinoshita
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Maria Sorokina
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Zlatko Puškadija
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M. Alice Pinto
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Andrea Guarracino
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Johannes Köster
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Bugra Oezdemir
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Santiago Royo-Sierra
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Nandan Deshpande
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Andrzej Oleksa
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Pedro João Rodrigues
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Franck Dedeine
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Enrique Bernal-Delgado
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Leonidas Charistos
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Max Schubach
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Hans-Rudolf Hotz
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Irfan Kandemir
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Claudio Satriano
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Aibyn Torekhanov
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Luc Cornet
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Vincent Hervé
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Mridul Johari
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Marin Kovačić
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Mehmet Tekman
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Kristian Peters
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Marjan Meurisse
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Zhanar Sheralieva
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Jose Raul Bravo Martinez
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Eliza Căuia
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Bonface Munyoki
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Szilvia Kusza
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Pierre Osteil
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Francisco Estupiñan-Romero
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Natalia Poiata
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Daniel Blankenberg
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Ulzhan Nuralieva
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Isaure Chauvot de Beauchêne
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Daphne Wijnbergen
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Denys Savchenko
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Pjotr Prins
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Javier González Galindo
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Michael Heuer
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Laurent Gil
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Natalia Martínez-Lizaga
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Marius van den Beek
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Riccardo Rossi Bernecoli
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Adrian Siceanu
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Diego Carvalho
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Nachida Tadrent
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Janez Prešern
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Leandro Liborio
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Damon-Lee Pointon
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Thomas Liener
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Marina Kennerson
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Peter Amstutz
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Jorge Ejarque
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Ying Sims
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Will Eagles
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Sanger Tree of Life Assembly method
type: Collection

ERGA Assembly Snakemake HiFi & HiC Pipelines
type: Collection

Genome Assembly Workflows for ERGA-BGE genomes
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IDR searcher
type: Collection

Sarah Beecroft
type: Person

Marco De La Pierre
type: Person

Julian Uszkoreit
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Camille Juigné
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Richard Lupat
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Johannes Köster
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Valentin Tilloy
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Michael Roach
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Elida Schneltzer
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Stian Soiland-Reyes
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Luiz Gadelha
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Asier Gonzalez-Uriarte
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Woosub Shin
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Ryan Patterson-Cross
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Anthony Bretaudeau
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Haris Zafeiropoulos
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Hrishikesh Dhondge
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Kim Philipp Jablonski
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Pasi Korhonen
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Marvin Martens
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Tazro Inutano
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Marlene Rezk
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Casper de Visser
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Jasper Ouwerkerk
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Benjamin Wingfield
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Anna Niehues
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Marie-Dominique Devignes
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Samuel Lambert
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Alejandra Escobar
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Tatiana Gurbich
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Katherine Farquharson
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Lucas Cruz
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Pablo Riesgo Ferreiro
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Felicita Gernhardt
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Andrey Prjibelski
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Xiaokang Zhang
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Zavolan Lab
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Mahnoor Zulfiqar
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Veit Schwämmle
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Anton Korobeynikov
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Tom Brown
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Luiz Gadelha
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Vasiliki Panagi
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Willem de Koning
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Jorrit Mesman
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Elisabetta Spinazzola
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Cristian Tatu
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Agata Kilar
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Jessica Gomez-Garrido
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Sagane Joye-Dind
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Pau Andrio
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Akshay Akshay
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Phuong Doan
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Davide Gurnari
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Johan Gustafsson
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David Yuan
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Justin Sonneck
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Zafran Hussain Shah
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Fernando Vázquez-Novoa
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Nandan Deshpande
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Michael Hall
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Rafael Terra
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Peter van Heusden
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Ian Brennan
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Bryan Raubenolt
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Matthias Haimel
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Carlos Oscar Sorzano Sanchez
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Paul Brack
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Wendi Bacon
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Romane Libouban
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KircherLab
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BCCM_ULC
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Computational Science at HZDR
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Big data in biomedicine
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TRE-FX
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NMR Workflow
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Guigó lab
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Statistical genetics
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Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation
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Bioinformatics Unit @ CRG
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BSC-CES
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ELIXIR Proteomics
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IRRI Bioinformatics Group
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Zavolan Lab
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Metabolomics-Reproducibility
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NGFF Tools
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Bioinformatics workflows for life science
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Helmholtz Scientific Project Workflow Platform
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BY-COVID Baseline Use Case: SARS-CoV-2 Vaccine(s) effectiveness in preventing SARS-CoV-2 infection
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Lake Erken modelling setup
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EOSC-Life WP3 OC Team, cross RI project
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ARA-dev
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Mendel Centre for Plant Genomics and Proteomics
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Metagenomic tools
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Pillar I: Manufacturing
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Polygenic Score Catalog
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MLme: Machine Learning Made Easy
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Dioscuri TDA
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MMV-Lab
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Tree of Life Genome Assembly
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EBP-Nor
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Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data
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COVID-19 PubSeq: Public SARS-CoV-2 Sequence Resource
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type: Project

Bioinformatics Laboratory for Genomics and Biodiversity (LBGB)
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Pangenome database project
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HP2NET - Framework for construction of phylogenetic networks on High Performance Computing (HPC) environment
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Generalized Open-Source Workflows for Atomistic Molecular Dynamics Simulations of Viral Helicases
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Italy-Covid-data-Portal
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EOSC-Life-WP6-Demos
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EOSC-Life WP3
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MOLGENIS
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Janis
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IAA-CSIC
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ODA
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FAME
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CHU Limoges - UF9481 Bioinformatique / CNR Herpesvirus
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HecatombDevelopment
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OpenEBench
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Bioinformatics and Biostatistics (BIO2 ) Core
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VIB Bioinformatics Core
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Single Cell Unit
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emo-bon
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CINECA
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Toxicology community
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Gyn Department
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Medizinisches Proteom-Center, Medical Bioinformatics
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AGRF BIO
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X-omics
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Andrew Lonie
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Anton Nekrutenko
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Bert Droesbeke
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Björn Grüning
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Dannon Baker
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Dave Bouvier
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Delphine Larivière
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Frederik Coppens
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Gildas Le Corguillé
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Ignacio Eguinoa
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James Taylor
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John Chilton
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Marius van den Beek
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Nate Coraor
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Nicholas Keener
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Sergei Kosakovsky Pond
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Simon Gladman
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Steven Weaver
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Wolfgang Maier
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Galaxy
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0_Input Dataset
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Galaxy
type: ComputerLanguage

0: View complete virus identification
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-02-04T09:05:08Z
date modified: 2023-02-13T14:06:45Z
description: Non-functional workflow to get a global view of possibilities for plant virus classification.
keywords: Virus, identification, exploration
license: Apache-2.0
producer: https://workflowhub.eu/projects/27
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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1: Plant virus detection with kraken2 (PE)
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-02-04T09:07:38Z
date modified: 2023-02-13T14:06:45Z
description: Metagenomic dataset taxonomic classification using kraken2
input: Data_R1.fastq
input: Data_R2.fastq
keywords: Virology, kraken
license: Apache-2.0
output: Kraken_report
output: Kraken_taxonomy_table
output: Kraken_taxonomy_table_modified
output: Taxonomic_prediction_report
producer: https://workflowhub.eu/projects/27
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Data_R1.fastq
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date created: 2021-02-04T09:10:04Z
date modified: 2023-02-13T14:06:45Z
description: Mapping against all plant virus then make contig out of the mapped reads then blast them.
input: Data_R1.fastq
input: Data_R2.fastq
keywords: Virology, mapping, Assembly, reads_selection, blast
license: Apache-2.0
output: Contigs
output: Log file
output: Paired-end forward
output: Paired-end reverse
output: Read_mapping_alignement
output: blastn result
output: mapped_read
output: reference_fasta
producer: https://workflowhub.eu/projects/27
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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3: Plant virus exploration
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-02-04T09:11:37Z
date modified: 2023-02-13T14:06:45Z
description: Just the cleaning then assembly of all reads. TO explore further follow one of the paths described in "Global view" (WF 0)
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license: Apache-2.0
output: Shovill on input dataset(s) Log file
output: Shovill on input dataset(s): Contigs
output: _anonymous_output_1
output: fastp on input dataset(s): HTML report
output: fastp on input dataset(s): Read 1 output
producer: https://workflowhub.eu/projects/27
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Alexey Sokolov
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Nextflow
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ENA SARS-CoV-2 Nanopore Amplicon Sequencing Analysis Workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Alexey Sokolov
creator: David F. Nieuwenhuijse
date created: 2021-02-12T11:46:24Z
date modified: 2023-11-24T10:02:56Z
description: A workflow for mapping and consensus generation of SARS-CoV2 whole genome amplicon nanopore data implemented in the Nextflow framework. Reads are mapped to a reference genome using Minimap2 after trimming the amplicon primers with a fixed length at both ends of the amplicons using Cutadapt. The consensus is called using Pysam based on a majority read support threshold per position of the Minimap2 alignment and positions with less than 30x coverage are masked using ‘N’.
is part of: https://workflowhub.eu/collections/16
license: GPL-3.0
producer: https://workflowhub.eu/projects/28
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Krisztian Papp
type: Person

Nextflow
type: ComputerLanguage

ENA SARS-CoV2 Variant Calling
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Krisztian Papp
date created: 2021-02-15T09:19:44Z
date modified: 2023-11-24T10:02:53Z
description: A pipeline for mapping, calling, and annotation of SARS-CoV2 variants.
is part of: https://workflowhub.eu/collections/16
license: Apache-2.0
producer: https://workflowhub.eu/projects/28
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Nextflow
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VariantCaller_GATK3.6
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/110
date created: 2021-05-21T08:45:49Z
date modified: 2023-01-16T13:47:03Z
description: Rare disease researchers workflow is that they submit their raw data (fastq), run the mapping and variant calling RD-Connect pipeline and obtain unannotated gvcf files to further submit to the RD-Connect GPAP or analyse on their own. This demonstrator focuses on the variant calling pipeline. The raw genomic data is processed using the RD-Connect pipeline ([Laurie et al., 2016](https://www.ncbi.nlm.nih.gov/pubmed/27604516)) running on the standards (GA4GH) compliant, interoperable container orchestration platform. This demonstrator will be aligned with the current implementation study on [Development of Architecture for Software Containers at ELIXIR and its use by EXCELERATE use-case communities](docs/Appendix%201%20-%20Project%20Plan%202018-biocontainers%2020171117.pdf) For this implementation, different steps are required: 1. Adapt the pipeline to CWL and dockerise elements 2. Align with IS efforts on software containers to package the different components (Nextflow) 3. Submit trio of Illumina NA12878 Platinum Genome or Exome to the GA4GH platform cloud (by Aspera or ftp server) 4. Run the RD-Connect pipeline on the container platform 5. Return corresponding gvcf files 6. OPTIONAL: annotate and update to RD-Connect playground instance N.B: The demonstrator might have some manual steps, which will not be in production. ## RD-Connect pipeline Detailed information about the RD-Connect pipeline can be found in [Laurie et al., 2016](https://www.ncbi.nlm.nih.gov/pubmed/?term=27604516) ![alt text](https://raw.githubusercontent.com/inab/Wetlab2Variations/eosc-life/docs/RD-Connect_pipeline.jpg) ## The applications **1\. Name of the application: Adaptor removal** Function: remove sequencing adaptors Container (readiness status, location, version): [cutadapt (v.1.18)](https://hub.docker.com/r/cnag/cutadapt) Required resources in cores and RAM: current container size 169MB Input data (amount, format, directory..): raw fastq Output data: paired fastq without adaptors **2\. Name…
is based on: https://workflowhub.eu/workflows/106
keywords: Nextflow, variant_calling
license: Apache-2.0
producer: https://workflowhub.eu/projects/31
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 3

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

chromosome
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conforms to: 1.0-RELEASE

fastq_files
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conforms to: 1.0-RELEASE

gqb
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conforms to: 1.0-RELEASE

known_indels_file
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known_sites_file
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readgroup_str
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reference_genome
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conforms to: 1.0-RELEASE

sample_name
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conforms to: 1.0-RELEASE

gvcf
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conforms to: 1.0-RELEASE

metrics
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conforms to: 1.0-RELEASE

VariantCaller_GATK3.6
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/110
creator: https://workflowhub.eu/people/4
date created: 2021-02-18T15:01:31Z
date modified: 2023-04-21T12:35:06Z
description: Rare disease researchers workflow is that they submit their raw data (fastq), run the mapping and variant calling RD-Connect pipeline and obtain unannotated gvcf files to further submit to the RD-Connect GPAP or analyse on their own. This demonstrator focuses on the variant calling pipeline. The raw genomic data is processed using the RD-Connect pipeline ([Laurie et al., 2016](https://www.ncbi.nlm.nih.gov/pubmed/27604516)) running on the standards (GA4GH) compliant, interoperable container orchestration platform. This demonstrator will be aligned with the current implementation study on [Development of Architecture for Software Containers at ELIXIR and its use by EXCELERATE use-case communities](docs/Appendix%201%20-%20Project%20Plan%202018-biocontainers%2020171117.pdf) For this implementation, different steps are required: 1. Adapt the pipeline to CWL and dockerise elements 2. Align with IS efforts on software containers to package the different components (Nextflow) 3. Submit trio of Illumina NA12878 Platinum Genome or Exome to the GA4GH platform cloud (by Aspera or ftp server) 4. Run the RD-Connect pipeline on the container platform 5. Return corresponding gvcf files 6. OPTIONAL: annotate and update to RD-Connect playground instance N.B: The demonstrator might have some manual steps, which will not be in production. ## RD-Connect pipeline Detailed information about the RD-Connect pipeline can be found in [Laurie et al., 2016](https://www.ncbi.nlm.nih.gov/pubmed/?term=27604516) ![alt text](https://raw.githubusercontent.com/inab/Wetlab2Variations/eosc-life/docs/RD-Connect_pipeline.jpg) ## The applications **1\. Name of the application: Adaptor removal** Function: remove sequencing adaptors Container (readiness status, location, version): [cutadapt (v.1.18)](https://hub.docker.com/r/cnag/cutadapt) Required resources in cores and RAM: current container size 169MB Input data (amount, format, directory..): raw fastq Output data: paired fastq without adaptors **2\. Name…
input: chromosome
input: fastq_files
input: gqb
input: known_indels_file
input: known_sites_file
input: readgroup_str
input: reference_genome
input: sample_name
keywords: CWL, variant_calling
license: Apache-2.0
output: gvcf
output: metrics
producer: https://workflowhub.eu/projects/31
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

#main/AF Filter
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#main/DP Filter
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#main/DP_ALT Filter
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#main/Number of Clusters
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#main/Variation data to report
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#main/gene products translations
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sars-cov-2-variation-reporting/COVID-19-VARIATION-REPORTING
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/222
date created: 2024-10-07T15:31:56Z
date modified: 2026-04-20T01:02:10Z
description: This workflow takes a VCF dataset of variants produced by any of the *-variant-calling workflows in https://github.com/galaxyproject/iwc/tree/main/workflows/sars-cov-2-variant-calling and generates tabular lists of variants by Samples and by Variant, and an overview plot of variants and their allele-frequencies.
input: #main/AF Filter
input: #main/DP Filter
input: #main/DP_ALT Filter
input: #main/Number of Clusters
input: #main/Variation data to report
input: #main/gene products translations
is based on: https://workflowhub.eu/workflows/109
is part of: https://workflowhub.eu/collections/16
is part of: https://workflowhub.eu/collections/2
license: MIT
output: #main/af_filter_threshold
output: #main/af_recalculated
output: #main/all_variants_all_samples
output: #main/by_variant_report
output: #main/collapsed_effects
output: #main/combined_variant_report
output: #main/dp_alt_filter_threshold
output: #main/dp_filter_threshold
output: #main/filtered_and_renamed_effects
output: #main/filtered_extracted_variants
output: #main/filtered_variants
output: #main/highest_impact_effects
output: #main/plot_number_of_clusters
output: #main/prefiltered_variants
output: #main/variant_frequency_plot
output: #main/variants_for_plotting
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 9

Genomics - Recombination and selection analysis
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Andrew Lonie
creator: Anton Nekrutenko
creator: Bert Droesbeke
creator: Björn Grüning
creator: Dannon Baker
creator: Dave Bouvier
creator: Delphine Larivière
creator: Frederik Coppens
creator: Gildas Le Corguillé
creator: Ignacio Eguinoa
creator: James Taylor
creator: John Chilton
creator: Marius van den Beek
creator: Nate Coraor
creator: Nicholas Keener
creator: Sergei Kosakovsky Pond
creator: Simon Gladman
creator: Steven Weaver
creator: Wolfgang Maier
date created: 2020-04-10T13:30:37Z
date modified: 2023-01-16T13:40:25Z
description: This workflow employs a recombination detection algorithm (GARD) developed by Kosakovsky Pond et al. and implemented in the hyphy package. More info can be found at https://covid19.galaxyproject.org/genomics/
input: 0_Input Dataset
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
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version: 1

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creator: https://workflowhub.eu/people/222
date created: 2025-03-26T09:51:25Z
date modified: 2026-04-20T01:01:51Z
description: COVID-19: variation analysis on ARTIC PE data --------------------------------------------- The workflow for Illumina-sequenced ampliconic data builds on the RNASeq workflow for paired-end data using the same steps for mapping and variant calling, but adds extra logic for trimming amplicon primer sequences off reads with the ivar package. In addition, this workflow uses ivar also to identify amplicons affected by primer-binding site mutations and, if possible, excludes reads derived from such "tainted" amplicons when calculating allele-frequencies of other variants.
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input: #main/ARTIC primers to amplicon assignments
input: #main/Minimum DP required after amplicon bias correction
input: #main/Minimum DP_ALT required after amplicon bias correction
input: #main/NC_045512.2 FASTA sequence of SARS-CoV-2
input: #main/Paired Collection
input: #main/Read removal maximum AF
input: #main/Read removal minimum AF
is based on: https://workflowhub.eu/workflows/110
is part of: https://workflowhub.eu/collections/16
is part of: https://workflowhub.eu/collections/2
keywords: covid-19, ARTIC, covid19.galaxyproject.org, BY-COVID
license: MIT
output: #main/amplicon_removal_output
output: #main/annotated_softfiltered_variants
output: #main/annotated_variants
output: #main/annotated_variants_stats
output: #main/bamqc_html_output
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output: #main/fastp_json_report
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output: #main/mapped_reads_stats
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output: #main/preliminary_variants_2
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output: #main/variants_fixed
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producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
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sars-cov-2-ont-artic-variant-calling/COVID-19-ARTIC-ONT
type: ComputationalWorkflow
type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/222
date created: 2021-12-21T03:01:07Z
date modified: 2025-12-12T02:01:36Z
description: COVID-19: variation analysis on ARTIC ONT data ---------------------------------------------- This workflow for ONT-sequenced ARTIC data is modeled after the alignment/variant-calling steps of the [ARTIC pipeline](https://artic.readthedocs.io/en/latest/). It performs, essentially, the same steps as that pipeline’s minion command, i.e. read mapping with minimap2 and variant calling with medaka. Like the Illumina ARTIC workflow it uses ivar for primer trimming. Since ONT-sequenced reads have a much higher error rate than Illumina-sequenced reads and are therefor plagued more by false-positive variant calls, this workflow does make no attempt to handle amplicons affected by potential primer-binding site mutations.
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input: #main/Minimum read length
input: #main/NC_045512.2 FASTA sequence of SARS-CoV-2
input: #main/ONT-sequenced reads
input: #main/Primer binding sites info in BED format
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is part of: https://workflowhub.eu/collections/2
keywords: covid-19, ONT, covid19.galaxyproject.org, ARTIC
license: MIT
output: #main/annotated_softfiltered_variants
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output: #main/minimap2_mapped_reads
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producer: https://workflowhub.eu/projects/33
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sd publisher: about.workflowhub.eu
version: 5

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sars-cov-2-se-illumina-wgs-variant-calling/COVID-19-SE-WGS-ILLUMINA
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/222
date created: 2025-03-26T12:00:25Z
date modified: 2026-04-12T01:02:44Z
description: This workflows performs single end read mapping with bowtie2 followed by sensitive variant calling across a wide range of AFs with lofreq
input: #main/NC_045512.2 FASTA sequence of SARS-CoV-2
input: #main/Single End Collection
is based on: https://workflowhub.eu/workflows/112
is part of: https://workflowhub.eu/collections/16
is part of: https://workflowhub.eu/collections/2
license: MIT
output: #main/annotated_variants
output: #main/annotated_variants_stats
output: #main/called_variant
output: #main/fastp_html_report
output: #main/mapped_reads
output: #main/markduplicates_reads
output: #main/markduplicates_stats
output: #main/preprocessed_reads
output: #main/preprocessing_and_mapping_reports
output: #main/realigned_deduplicated_mapped_reads
output: #main/realigned_deduplicated_mapped_reads_with_indel_quals
output: #main/soft_filtered_variants
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 6

Galaxy
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sars-cov-2-pe-illumina-wgs-variant-calling/COVID-19-PE-WGS-ILLUMINA
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/222
date created: 2021-12-21T03:01:05Z
date modified: 2025-12-12T02:01:33Z
description: COVID-19: variation analysis on WGS PE data ------------------------------------------- This workflows performs paired end read mapping with bwa-mem followed by sensitive variant calling across a wide range of AFs with lofreq and variant annotation with snpEff 4.5covid19.
input: #main/NC_045512.2 FASTA sequence of SARS-CoV-2
input: #main/Paired Collection
is based on: https://workflowhub.eu/workflows/113
is part of: https://workflowhub.eu/collections/16
is part of: https://workflowhub.eu/collections/2
keywords: covid-19, covid19.galaxyproject.org, emergen_validated, Virology
license: MIT
output: #main/Map with BWA-MEM on input dataset(s) (mapped reads in BAM format)
output: #main/annotated_variants
output: #main/annotated_variants_stats
output: #main/called_variants
output: #main/fastp_html_report
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output: #main/preprocessing_and_mapping_reports
output: #main/realigned_deduplicated_filtered_mapped_reads
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producer: https://workflowhub.eu/projects/33
programming language: Galaxy
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Trinity RNA Assembly
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Marco De La Pierre
date created: 2021-03-26T12:58:59Z
date modified: 2023-01-16T13:49:39Z
description: A porting of the Trinity RNA assembly pipeline, https://trinityrnaseq.github.io, that uses Nextflow to handle the underlying sub-tasks. This enables additional capabilities to better use HPC resources, such as packing of tasks to fill up nodes and use of node-local disks to improve I/O. By design, the pipeline separates the workflow logic (main file) and the cluster-specific configuration (config files), improving portability. Based on a pipeline by Sydney Informatics Hub: https://github.com/Sydney-Informatics-Hub/SIH-Raijin-Trinity
is based on: https://workflowhub.eu/workflows/114
is part of: https://workflowhub.eu/collections/6
keywords: Assembly, Transcriptomics, RNASEQ, Nextflow
license: GPL-3.0
producer: https://workflowhub.eu/projects/30
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version: 2

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Object tracking using CellProfiler
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-03-21T18:12:10Z
date modified: 2023-07-03T10:16:04Z
description: Workflow for tracking objects in Cell Profiler: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/object-tracking-using-cell-profiler/tutorial.html
keywords: CellProfiler, imaging, Galaxy, image processing
license: Apache-2.0
producer: https://workflowhub.eu/projects/1
producer: https://workflowhub.eu/projects/167
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
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Workflow constructed from history 'test dwc from PNDB Data package EML DwC annotations'
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type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/51
date created: 2021-04-09T15:45:27Z
date modified: 2023-11-09T21:04:38Z
description: Workflow to take DataOne data packages (raw datasets + metadata written in Ecological Metadata Standard) as input and create a DwC occurence.csv file almost ready to put in a Dawrin core Archive using eml-annotations at the attribute level
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input: Kakila_database_of_marine_mammal_observation_data_in_the_AGOA_sanctuary_-_French_Antilles.xml
keywords: DataOne, Data package, EML, Ecological metadata language, eml-annotation, Darwin core, Galaxy-E, Galaxy
license: MIT
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producer: https://workflowhub.eu/projects/19
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COSIFER
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creator: https://workflowhub.eu/people/110
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date created: 2021-05-05T15:50:31Z
date modified: 2023-04-21T11:04:20Z
description: # COnSensus Interaction Network InFErence Service Inference framework for reconstructing networks using a consensus approach between multiple methods and data sources. ![alt text](https://github.com/PhosphorylatedRabbits/cosifer/raw/master/docs/_static/logo.png) ## Reference [Manica, Matteo, Charlotte, Bunne, Roland, Mathis, Joris, Cadow, Mehmet Eren, Ahsen, Gustavo A, Stolovitzky, and María Rodríguez, Martínez. "COSIFER: a python package for the consensus inference of molecular interaction networks".Bioinformatics (2020)](https://doi.org/10.1093/bioinformatics/btaa942).
input: data_matrix
input: gmt_filepath
input: index_col
input: outdir
input: samples_on_rows
input: separator
keywords: cosifer, cancer, pediatric, rna-seq
license: Apache-2.0
output: resdir
producer: https://workflowhub.eu/projects/35
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

COSIFER
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/110
creator: https://workflowhub.eu/people/4
date created: 2021-05-05T15:53:28Z
date modified: 2023-04-21T11:04:40Z
description: # COnSensus Interaction Network InFErence Service Inference framework for reconstructing networks using a consensus approach between multiple methods and data sources. ![alt text](https://raw.githubusercontent.com/PhosphorylatedRabbits/cosifer/master/docs/_static/logo.png) ## Reference [Manica, Matteo, Charlotte, Bunne, Roland, Mathis, Joris, Cadow, Mehmet Eren, Ahsen, Gustavo A, Stolovitzky, and María Rodríguez, Martínez. "COSIFER: a python package for the consensus inference of molecular interaction networks".Bioinformatics (2020)](https://doi.org/10.1093/bioinformatics/btaa942).
keywords: cosifer, cancer, pediatric, rna-seq
license: Apache-2.0
producer: https://workflowhub.eu/projects/35
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

(old) SARS-COV2 version of the V-Pipe workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/22
creator: 11#
creator: Niko Beerenwinkel
creator: Susana Posada Céspedes
date created: 2020-04-10T13:46:55Z
date modified: 2023-01-16T13:40:31Z
description: A version of V-pipe (analysis of next generation sequencing (NGS) data from viral pathogens) specifically adapted to analyze high-throughput sequencing data of SARS-CoV-2.
license: Apache-2.0
producer: https://workflowhub.eu/projects/7
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Björn Grüning
type: Person

Frank von Delft
type: Person

Gianmauro Cuccuru
type: Person

Jack Scantlebury
type: Person

Rachael Skyner
type: Person

Simon Bray
type: Person

Susan Leung
type: Person

Tim Dudgeon
type: Person

0_Input Dataset
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conforms to: 1.0-RELEASE

Galaxy
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Jupyter
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Jupyter Notebook Protein MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T09:14:04Z
date modified: 2026-03-24T09:19:09Z
description: # Protein MD Setup tutorial using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Lysozyme** protein (PDB code 1AKI). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/120
is part of: https://workflowhub.eu/collections/1
is part of: https://workflowhub.eu/collections/19
is part of: https://workflowhub.eu/collections/3
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 8

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

GROMACS grompp configuration dictionary
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GROMACS grompp configuration dictionary
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GROMACS mdrun configuration dictionary
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Input file
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Editconf configuration dictionary
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GROMACS grompp configuration dictionary
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Genion configuration dictionary
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GROMACS grompp configuration dictionary
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conforms to: 1.0-RELEASE

GROMACS make_ndx configuration dictionary
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GROMACS grompp configuration dictionary
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whole workflow output
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Molecular Dynamics Simulation
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-05-20T14:41:19Z
date modified: 2023-01-16T13:49:52Z
description: CWL version of the md_list.py workflow for HPC. This performs a system setup and runs a molecular dynamics simulation on the structure passed to this workflow. This workflow uses the md\_gather.cwl sub-workflow to gather the outputs together to return these. To work with more than one structure this workflow can be called from either the md\_launch.cwl workflow, or the md\_launch\_mutate.cwl workflow. These use scatter for parallelising the workflow. md\_launch.cwl operates on a list of individual input molecule files. md\_launch\_mutate.cwl operates on a single input molecule file, and a list of mutations to apply to that molecule. Within that list of mutations, a value of 'WT' will indicate that the molecule should be simulated without any mutation being applied.
input: GROMACS grompp configuration dictionary
input: GROMACS mdrun configuration dictionary
input: Input file
input: Editconf configuration dictionary
input: Genion configuration dictionary
input: GROMACS make_ndx configuration dictionary
license: Apache-2.0
output: whole workflow output
producer: https://workflowhub.eu/projects/11
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

endpoint
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conforms to: 1.0-RELEASE

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SPARQL query (in a file) on graph database
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-05-26T10:51:51Z
date modified: 2023-01-16T13:49:55Z
description: A workflow querying on an endpoint of a graph database by a file containing a SPARQL query.
input: endpoint
input: query_file
is based on: https://workflowhub.eu/workflows/122
license: Apache-2.0
producer: EOSC-Life-WP6-Demos
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 2

Temperature timeseries (csv)
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conforms to: 1.0-RELEASE

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stripes_monthly_temperatures
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Galaxy
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Compute daily and monthly mean from meteorological station measurements
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-05-23T19:28:06Z
date modified: 2023-01-16T13:50:01Z
description: This workflow is used to process timeseries from meteorological stations in Finland but can be applied to any timeseries according it follows the same format. Take a temperature timeseries from any meteorological station. Input format is csv and it must be standardized with 6 columns: 1. Year (ex: 2021) 2. month (ex: 1) 3. day (ex: 15) 4. Time (ex: 16:56) 5. Time zone (such as UTC) 6. Air temperature (degC)
input: Temperature timeseries (csv)
keywords: Climate, eosc-nordic, observation
license: GPL-3.0
output: daily_barchart
output: daily_mean_timeseries
output: monthly_mean_timeseries
output: stripes_daily_temperatures
output: stripes_monthly_temperatures
producer: https://workflowhub.eu/projects/18
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Kraken_report
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conforms to: 1.0-RELEASE

Kraken_taxonomy_table
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conforms to: 1.0-RELEASE

Taxonomic_prediction_report
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conforms to: 1.0-RELEASE

Galaxy
type: ComputerLanguage

1: Plant virus detection with kraken2 (SE)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-06-17T09:14:19Z
date modified: 2023-02-13T14:06:45Z
description: Metagenomic dataset taxonomic classification using kraken2
keywords: Virology, kraken
license: Apache-2.0
output: Kraken_report
output: Kraken_taxonomy_table
output: Kraken_taxonomy_table_modified
output: Taxonomic_prediction_report
producer: https://workflowhub.eu/projects/27
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

MOLGENIS/VIP: Variant Interpretation Pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-06-21T08:33:47Z
date modified: 2024-06-12T09:57:49Z
description: Variant Interpretation Pipeline (VIP) that annotates, filters and reports prioritized causal variants in humans, see https://github.com/molgenis/vip for more information.
keywords: Annotation, Report, VCF, Classification, SV, Pipeline, Bioinformatics, Genomics, Workflows, Java, SNPs, variation, Nextflow
license: LGPL-3.0
producer: MOLGENIS
sd publisher: about.workflowhub.eu
version: 1

Daniël Wijnbergen
type: Person

Eleni Mina
type: Person

Snakemake
type: ComputerLanguage

EJP-RD WP13 case-study CAKUT momix analysis
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/110
creator: https://workflowhub.eu/people/134
creator: https://workflowhub.eu/people/135
creator: https://workflowhub.eu/people/136
creator: https://workflowhub.eu/people/251
creator: https://workflowhub.eu/people/4
creator: Matthias Haimel
creator: Daniël Wijnbergen
creator: Eleni Mina
date created: 2021-06-23T10:42:46Z
date modified: 2022-10-27T16:39:25Z
description: Joint multi-omics dimensionality reduction approaches for CAKUT data using peptidome and proteome data **Brief description** In (Cantini et al. 2020), Cantini et al. evaluated 9 representative joint dimensionality reduction (jDR) methods for multi-omics integration and analysis and . The methods are Regularized Generalized Canonical Correlation Analysis (RGCCA), Multiple co-inertia analysis (MCIA), Multi-Omics Factor Analysis (MOFA), Multi-Study Factor Analysis (MSFA), iCluster, Integrative NMF (intNMF), Joint and Individual Variation Explained (JIVE), tensorial Independent Component Analysis (tICA), and matrix-tri-factorization (scikit-fusion) (Tenenhaus, Tenenhaus, and Groenen 2017; Bady et al. 2004; Argelaguet et al. 2018; De Vito et al. 2019; Shen, Olshen, and Ladanyi 2009; Chalise and Fridley 2017; Lock et al. 2013; Teschendorff et al. 2018; Žitnik and Zupan 2015). The authors provided their benchmarking procedure, multi-omics mix (momix), as Jupyter Notebook on GitHub (https://github.com/ComputationalSystemsBiology/momix-notebook) and project environment through Conda. In momix, the factorization methods are called from an R script, and parameters of the methods are also set in that script. We did not modify the parameters of the methods in the provided script. We set factor number to 2.
keywords: rare diseases, workflow, Proteomics, protein, mirna prediction
license: GPL-3.0
producer: https://workflowhub.eu/projects/40
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Jupyter
type: ComputerLanguage

Jupyter Notebook Protein-ligand Docking tutorial (Cluster90)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/37
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T15:15:39Z
date modified: 2026-03-24T15:18:08Z
description: # Protein-ligand Docking tutorials using BioExcel Building Blocks (biobb) This tutorials aim to illustrate the process of **protein-ligand docking**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular examples used are based on the **Mitogen-activated protein kinase 14** (p38-α) protein (PDB code [3HEC](https://www.rcsb.org/structure/3HEC)), a well-known **Protein Kinase enzyme**, in complex with the FDA-approved **Imatinib** (PDB Ligand code [STI](https://www.rcsb.org/ligand/STI), DrugBank Ligand Code [DB00619](https://go.drugbank.com/drugs/DB00619)) and **Dasatinib** (PDB Ligand code [1N1](https://www.rcsb.org/ligand/1N1), DrugBank Ligand Code [DB01254](https://go.drugbank.com/drugs/DB01254)), small **kinase inhibitors** molecules used to treat certain types of **cancer**. The tutorials will guide you through the process of identifying the **active site cavity** (pocket) without previous knowledge, and the final prediction of the **protein-ligand complex**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/127
is part of: https://workflowhub.eu/collections/1
is part of: https://workflowhub.eu/collections/19
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 8

Jupyter
type: ComputerLanguage

Jupyter Notebook Protein-ligand Docking tutorial (PDBe REST API)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/37
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T15:18:40Z
date modified: 2026-03-24T15:20:24Z
description: # Protein-ligand Docking tutorials using BioExcel Building Blocks (biobb) This tutorials aim to illustrate the process of **protein-ligand docking**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular examples used are based on the **Mitogen-activated protein kinase 14** (p38-α) protein (PDB code [3HEC](https://www.rcsb.org/structure/3HEC)), a well-known **Protein Kinase enzyme**, in complex with the FDA-approved **Imatinib** (PDB Ligand code [STI](https://www.rcsb.org/ligand/STI), DrugBank Ligand Code [DB00619](https://go.drugbank.com/drugs/DB00619)) and **Dasatinib** (PDB Ligand code [1N1](https://www.rcsb.org/ligand/1N1), DrugBank Ligand Code [DB01254](https://go.drugbank.com/drugs/DB01254)), small **kinase inhibitors** molecules used to treat certain types of **cancer**. The tutorials will guide you through the process of identifying the **active site cavity** (pocket) without previous knowledge, and the final prediction of the **protein-ligand complex**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/128
is part of: https://workflowhub.eu/collections/1
is part of: https://workflowhub.eu/collections/19
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 7

Jupyter
type: ComputerLanguage

Jupyter Notebook Protein-ligand Docking tutorial (Fpocket)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/37
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T15:22:14Z
date modified: 2026-03-24T15:24:30Z
description: # Protein-ligand Docking tutorials using BioExcel Building Blocks (biobb) This tutorials aim to illustrate the process of **protein-ligand docking**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular examples used are based on the **Mitogen-activated protein kinase 14** (p38-α) protein (PDB code [3HEC](https://www.rcsb.org/structure/3HEC)), a well-known **Protein Kinase enzyme**, in complex with the FDA-approved **Imatinib** (PDB Ligand code [STI](https://www.rcsb.org/ligand/STI), DrugBank Ligand Code [DB00619](https://go.drugbank.com/drugs/DB00619)) and **Dasatinib** (PDB Ligand code [1N1](https://www.rcsb.org/ligand/1N1), DrugBank Ligand Code [DB01254](https://go.drugbank.com/drugs/DB01254)), small **kinase inhibitors** molecules used to treat certain types of **cancer**. The tutorials will guide you through the process of identifying the **active site cavity** (pocket) without previous knowledge, and the final prediction of the **protein-ligand complex**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/129
is part of: https://workflowhub.eu/collections/1
is part of: https://workflowhub.eu/collections/19
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 7

Cheminformatics - Enumerate ligands for docking
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Björn Grüning
creator: Frank von Delft
creator: Gianmauro Cuccuru
creator: Jack Scantlebury
creator: Rachael Skyner
creator: Simon Bray
creator: Susan Leung
creator: Tim Dudgeon
date created: 2020-04-10T14:17:44Z
date modified: 2023-01-16T13:40:34Z
description: This workflow is used form the preparation of protein and ligands for docking. More info can be found at https://covid19.galaxyproject.org/cheminformatics/
input: 0_Input Dataset
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Björn Grüning
type: Person

Frank von Delft
type: Person

Gianmauro Cuccuru
type: Person

Jack Scantlebury
type: Person

Rachael Skyner
type: Person

Simon Bray
type: Person

Susan Leung
type: Person

Tim Dudgeon
type: Person

Mpro-x0195_0_apo-desolv_pdb
type: FormalParameter
conforms to: 1.0-RELEASE

hits_frankenstein_17_sdf
type: FormalParameter
conforms to: 1.0-RELEASE

Galaxy
type: ComputerLanguage

Jupyter
type: ComputerLanguage

Jupyter Notebook Amber Protein MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T10:00:32Z
date modified: 2026-03-26T10:02:19Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/130
is part of: https://workflowhub.eu/collections/1
is part of: https://workflowhub.eu/collections/19
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 7

Jupyter
type: ComputerLanguage

Jupyter Notebook Amber Protein Ligand Complex MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T10:33:46Z
date modified: 2026-03-26T11:11:40Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/131
is part of: https://workflowhub.eu/collections/1
is part of: https://workflowhub.eu/collections/19
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 7

Jupyter
type: ComputerLanguage

Jupyter Notebook Amber Constant pH MD Setup tutorial
type: ComputationalWorkflow
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creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T11:41:41Z
date modified: 2026-03-26T11:43:35Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/132
is part of: https://workflowhub.eu/collections/1
is part of: https://workflowhub.eu/collections/19
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 7

Nextflow
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ORSON: workflow for prOteome and tRanScriptome functiOnal aNnotation
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/145
creator: https://workflowhub.eu/people/146
creator: https://workflowhub.eu/people/147
creator: https://workflowhub.eu/people/148
creator: https://workflowhub.eu/people/150
date created: 2021-07-08T14:18:03Z
date modified: 2023-01-16T13:50:20Z
description: ORSON combine state-of-the-art tools for annotation processes within a Nextflow pipeline: sequence similarity search (PLAST, BLAST or Diamond), functional annotation retrieval (BeeDeeM) and functional prediction (InterProScan). When required, BUSCO completness evaluation and eggNOG Orthogroup annotation can be activated. While ORSON results can be analyzed through the command-line, it also offers the possibility to be compatible with BlastViewer or Blast2GO graphical tools.
keywords: Annotation, Transcriptomics, Genomics, Proteomics, Nextflow
license: MIT
producer: https://workflowhub.eu/projects/47
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

IWC
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Galaxy
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#main/Run accessions
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parallel-accession-download/main
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Marius van den Beek
creator: IWC
date created: 2024-06-11T02:01:57Z
date modified: 2026-04-20T01:01:51Z
description: Downloads fastq files for sequencing run accessions provided in a text file using fasterq-dump. Creates one job per listed run accession.
input: #main/Run accessions
is based on: https://workflowhub.eu/workflows/137
license: MIT
output: #main/Paired End Reads
output: #main/Single End Reads
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 9

Galaxy
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#main/chrom_pos_ref_called_variants
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#main/consensus
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sars-cov-2-consensus-from-variation/COVID-19-CONSENSUS-CONSTRUCTION
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/222
date created: 2025-03-26T12:00:41Z
date modified: 2026-04-20T01:01:56Z
description: Build a consensus sequence from FILTER PASS variants with intrasample allele-frequency above a configurable consensus threshold. Hard-mask regions with low coverage (but not consensus variants within them) and ambiguous sites.
input: #main/Depth-threshold for masking
input: #main/Reference genome
input: #main/Variant calls
input: #main/aligned reads data for depth calculation
input: #main/min-AF for consensus variant
input: #main/min-AF for failed variants
is based on: https://workflowhub.eu/workflows/138
is part of: https://workflowhub.eu/collections/16
is part of: https://workflowhub.eu/collections/2
license: MIT
output: #main/1_based_masking_regions
output: #main/called_variant_sites
output: #main/chrom_pos_ref_called_variants
output: #main/chrom_pos_ref_failed_variants
output: #main/consensus
output: #main/consensus_af_threshold
output: #main/consensus_variants
output: #main/coverage_depth
output: #main/depth_threshold
output: #main/failed_variant_sites
output: #main/filter_failed_variants
output: #main/low_cov_regions
output: #main/low_cov_regions_plus_filter_failed
output: #main/low_cov_regions_plus_filter_failed_combined
output: #main/masking_regions
output: #main/multisample_consensus_fasta
output: #main/non_consensus_af_threshold
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 9

Cheminformatics - Active site generation
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Björn Grüning
creator: Frank von Delft
creator: Gianmauro Cuccuru
creator: Jack Scantlebury
creator: Rachael Skyner
creator: Simon Bray
creator: Susan Leung
creator: Tim Dudgeon
date created: 2020-04-10T14:25:46Z
date modified: 2023-01-16T13:40:43Z
description: This workflow generates a file describing the active site of the protein for each of the fragment screening crystal structures using rDock s rbcavity. It also creates a single hybrid molecule that contains all the ligands - the "frankenstein" ligand. More info can be found at https://covid19.galaxyproject.org/cheminformatics/
input: Mpro-x0195_0_apo-desolv_pdb
input: hits_frankenstein_17_sdf
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Björn Grüning
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Frank von Delft
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Gianmauro Cuccuru
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Jack Scantlebury
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Rachael Skyner
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Simon Bray
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Susan Leung
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Tim Dudgeon
type: Person

0_Input Dataset
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Galaxy
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Brian M Forde
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Adam D Irwin
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David L Paterson
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David M Whiley
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Leah W Roberts
type: Person

Mark A Schembri
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Minh-Duy Phan
type: Person

Nguyen Thi Khanh Nhu
type: Person

Patrick N A Harris
type: Person

Scott A Beatson
type: Person

Nextflow
type: ComputerLanguage

microPIPE: a pipeline for high-quality bacterial genome construction using ONT and Illumina sequencing
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/140
creator: Brian M Forde
creator: Adam D Irwin
creator: David L Paterson
creator: David M Whiley
creator: Leah W Roberts
creator: Mark A Schembri
creator: Minh-Duy Phan
creator: Nguyen Thi Khanh Nhu
creator: Patrick N A Harris
creator: Scott A Beatson
date created: 2021-08-09T00:17:36Z
date modified: 2023-01-16T13:51:29Z
description: microPIPE was developed to automate high-quality complete bacterial genome assembly using Oxford Nanopore Sequencing in combination with Illumina sequencing. To build microPIPE we evaluated the performance of several tools at each step of bacterial genome assembly, including basecalling, assembly, and polishing. Results at each step were validated using the high-quality ST131 Escherichia coli strain EC958 (GenBank: HG941718.1). After appraisal of each step, we selected the best combination of tools to achieve the most consistent and best quality bacterial genome assemblies. The workflow below summarises the different steps of the pipeline (with each selected tool) and the approximate run time (using GPU basecalling, averaged over 12 E. coli isolates sequenced on a R9.4 MinION flow cell). Dashed boxes correspond to optional steps in the pipeline. Micropipe has been written in Nextflow and uses Singularity containers. It can use both GPU and CPU resources. For more information please see our publication here: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-021-07767-z Infrastructure\_deployment\_metadata: Zeus (Pawsey)
is part of: https://workflowhub.eu/collections/6
keywords: ONT, bacterial-genomics, Assembly, Nextflow, workflow
license: GPL-3.0
producer: https://workflowhub.eu/projects/41
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Snakemake
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HI-FRIENDS HI data cube source finding and characterization
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-08-09T20:25:49Z
date modified: 2023-01-16T13:51:30Z
description: This repository contains the workflow used to find and characterize the HI sources in the data cube of the SKA Data Challenge 2. It was developed to process a simulated [SKA data cube](https://sdc2.astronomers.skatelescope.org/sdc2-challenge/data) data cube, but can be adapted for clean HI data cubes from other radio observatories. The workflow is managed and executed using snakemake workflow management system. It uses [https://spectral-cube.readthedocs.io/en/latest/](http://) based on [https://dask.org/](http://) parallelization tool and [https://www.astropy.org/](http://) suite to divide the large cube in smaller pieces. On each of the subcubes, we execute [https://github.com/SoFiA-Admin/SoFiA-2](http://) for masking the subcubes, find sources and characterize their properties. Finally, the individual catalogs are cleaned, concatenated into a single catalog, and duplicates from the overlapping regions are eliminated. Some diagnostic plots are produced using Jupyter notebook. The documentation can be found in the [Documentation page](https://hi-friends-sdc2.readthedocs.io/en/latest/index.html). The workflow and the results can be cited in the [Zenodo record](https://doi.org/10.5281/zenodo.5167659).
keywords: SKA, radio interferometry
license: GPL-3.0
producer: IAA-CSIC
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

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Galaxy
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16S_biodiversity_BIOM
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/162
date created: 2021-08-11T04:12:04Z
date modified: 2024-04-17T04:16:05Z
description: This is a Galaxy workflow that uses to convert the16S BIOM file to table and figures. It is part of the metaDEGalaxy workflow MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data.
is part of: https://workflowhub.eu/collections/6
keywords: MetaDEGalaxy
license: GPL-3.0
output: _anonymous_output_1
output: _anonymous_output_2
output: _anonymous_output_3
output: _anonymous_output_4
output: _anonymous_output_5
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Shell Script
type: ComputerLanguage

Germline-ShortV @ NCI-Gadi
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/122
creator: https://workflowhub.eu/people/123
creator: https://workflowhub.eu/people/141
creator: https://workflowhub.eu/people/258
date created: 2021-08-17T04:35:21Z
date modified: 2025-07-25T02:04:55Z
description: Germline-ShortV @ NCI-Gadi is an implementation of the BROAD Institute's best practice workflow for germline short variant discovery. This implementation is optimised for the National Compute Infrastucture's Gadi HPC, utilising scatter-gather parallelism to enable use of multiple nodes with high CPU or memory efficiency. This workflow requires sample BAM files, which can be generated using the [Fastq-to-bam @ NCI-Gadi](https://workflowhub.eu/workflows/146) pipeline. Germline-ShortV can be applied to model and non-model organisms (including non-diploid organisms). Infrastructure\_deployment\_metadata: Gadi (NCI)
is part of: https://workflowhub.eu/collections/6
keywords: GATK4, variant_calling, WGS, SNPs, INDELs, HaplotyeCaller, Germline, BROAD, Genomics, genome, DNA, DNA-seq
license: GPL-3.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Workflow Description Language
type: ComputerLanguage
alternate name: WDL

GATK4 Fastq to joint-called cohort VCF with Cromwell on SLURM
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-08-17T04:42:40Z
date modified: 2023-01-16T13:51:44Z
description: # SLURM HPC Cromwell implementation of GATK4 germline variant calling pipeline See the [GATK](https://gatk.broadinstitute.org/hc/en-us) website for more information on this toolset ## Assumptions - Using hg38 human reference genome build - Running using HPC/SLURM scheduling. This repo was specifically tested on Pawsey Zeus machine, primarily running in the `/scratch` partition. - Starting from short-read Illumina paired-end fastq files as input ### Dependencies The following versions have been tested and work, but GATK and Cromwell are regularly updated and so one must consider whether they would like to use newer versions of these tools. - BWA/0.7.15 - GATK v4.0.6.0 - SAMtools/1.5 - picard/2.9 - Python/2.7 - Cromwell v61 ## Quick start guide ### Installing and preparing environment for GATK4 with Cromwell 1. Clone repository ``` git clone https://github.com/SarahBeecroft/slurmCromwellGATK4.git cd slurmCromwellGATK4 chmod +x *.sh ``` 2. Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) if you haven’t already. This is best placed in your `/group` directory to avoid filling your small `/home` directory, or being purged is placed in the `/scratch` directory. 3. Create Conda environment using the supplied conda environment file ``` conda env create --file gatk4_pipeline.yml ``` 3. Download the necessary .jar files - The Cromwell workfow orchestration engine can be downloaded from https://github.com/broadinstitute/cromwell/releases/ - GATK can be downloaded from https://github.com/broadinstitute/gatk/releases. Unzip the file with `unzip` - Picard can be downloaded from https://github.com/broadinstitute/picard/releases/ 4. If you do not have the resource bundle files already, these need to be downloaded. In future they will be cached on Pawsey systems. The bundle data should be download from the [Google Cloud bucket](https://console.cloud.google.com/storage/browser/genomics-public-data/references/hg38/v0;tab=objects?_ga=2.98248159.1769807612.1582055494-2…
is part of: https://workflowhub.eu/collections/6
keywords: GATK4, Genomics, Alignment, variant_calling, SNPs, INDELs
license: Apache-2.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/42
programming language: Workflow Description Language
sd publisher: about.workflowhub.eu
version: 1

Andrey Bliznyuk
type: Person

Ben Evans
type: Person

Ben Menadue
type: Person

Matthew Downton
type: Person

Rika Kobayashi
type: Person

Shell Script
type: ComputerLanguage

Trinity @ NCI-Gadi
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/122
creator: https://workflowhub.eu/people/123
creator: https://workflowhub.eu/people/141
creator: Andrey Bliznyuk
creator: Ben Evans
creator: Ben Menadue
creator: Matthew Downton
creator: Rika Kobayashi
date created: 2021-08-17T04:44:57Z
date modified: 2023-01-16T13:51:44Z
description: Description: Trinity @ NCI-Gadi contains a staged [Trinity](https://github.com/trinityrnaseq/trinityrnaseq/wiki) workflow that can be run on the National Computational Infrastructure’s (NCI) Gadi supercomputer. Trinity performs de novo transcriptome assembly of RNA-seq data by combining three independent software modules Inchworm, Chrysalis and Butterfly to process RNA-seq reads. The algorithm can detect isoforms, handle paired-end reads, multiple insert sizes and strandedness. Infrastructure\_deployment\_metadata: Gadi (NCI)
is part of: https://workflowhub.eu/collections/6
keywords: Assembly, Transcriptomics, trinity, NCI, RNASEQ, rna, rna-seq, Gadi, scalable, PBS
license: Apache-2.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Andrey Bliznyuk
type: Person

Ben Menadue
type: Person

Matthew Downton
type: Person

Rika Kobayashi
type: Person

Yue Sun
type: Person

Shell Script
type: ComputerLanguage

Fastq-to-bam @ NCI-Gadi
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/122
creator: https://workflowhub.eu/people/123
creator: https://workflowhub.eu/people/141
creator: https://workflowhub.eu/people/258
creator: Andrey Bliznyuk
creator: Ben Menadue
creator: Matthew Downton
creator: Rika Kobayashi
creator: Yue Sun
date created: 2021-08-17T04:45:57Z
date modified: 2025-07-25T01:52:31Z
description: Fastq-to-BAM @ NCI-Gadi is a genome alignment workflow that takes raw FASTQ files, aligns them to a reference genome and outputs analysis ready BAM files. This workflow is designed for the National Computational Infrastructure's (NCI) Gadi supercompter, leveraging multiple nodes on NCI Gadi to run all stages of the workflow in parallel, either massively parallel using the scatter-gather approach or parallel by sample. It consists of a number of stages and follows the BROAD Institute's best practice recommendations. Infrastructure\_deployment\_metadata: Gadi (NCI)
is part of: https://workflowhub.eu/collections/6
keywords: Genomics, Alignment, BROAD, WGS, BWA-mem, scalable, NCI, Gadi, PBS, genome, DNA, mapping
license: GPL-3.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Workflow Description Language
type: ComputerLanguage
alternate name: WDL

GATK4 Fastq to joint-called cohort VCF with Cromwell on local cluster (no job scheduler)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-08-17T04:47:53Z
date modified: 2023-01-16T13:51:45Z
description: # Local Cromwell implementation of GATK4 germline variant calling pipeline See the [GATK](https://gatk.broadinstitute.org/hc/en-us) website for more information on this toolset ## Assumptions - Using hg38 human reference genome build - Running 'locally' i.e. not using HPC/SLURM scheduling, or containers. This repo was specifically tested on Pawsey Nimbus 16 CPU, 64GB RAM virtual machine, primarily running in the `/data` volume storage partition. - Starting from short-read Illumina paired-end fastq files as input ### Dependencies The following versions have been tested and work, but GATK and Cromwell are regularly updated and so one must consider whether they would like to use newer versions of these tools. - BWA/0.7.15 - GATK v4.0.6.0 - SAMtools/1.5 - picard/2.9 - Python/2.7 - Cromwell v61 ## Quick start guide ### Installing and preparing environment for GATK4 with Cromwell 1. Clone repository ``` git clone https://github.com/SarahBeecroft/cromwellGATK4.git cd cromwellGATK4 chmod 777 *.sh ``` 2. Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) if you haven’t already. Create Conda environment using the supplied conda environment file ``` conda env create --file gatk4_pipeline.yml ``` 3. Download the necessary .jar files - The Cromwell workfow orchestration engine can be downloaded from https://github.com/broadinstitute/cromwell/releases/ - GATK can be downloaded from https://github.com/broadinstitute/gatk/releases. Unzip the file with `unzip` - Picard can be downloaded from https://github.com/broadinstitute/picard/releases/ 4. Upload the resource bundle file from IRDS using rclone or filezilla and unpack it with `tar xzvf resource.tar.gz`. Note that the `hg38_wgs_scattered_calling_intervals.txt` will need to be to generated using the following: ``` cd find `pwd` -name "scattered.interval_list" -print | sort > hg38_wgs_scattered_calling_intervals.txt ``` 5. Set up the config files. Files that you need to edit with the correct paths to your data/jar …
is part of: https://workflowhub.eu/collections/6
keywords: Alignment, GATK4, Genomics, variant_calling, SNPs, INDELs, workflow
license: Apache-2.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/42
programming language: Workflow Description Language
sd publisher: about.workflowhub.eu
version: 1

Shell Script
type: ComputerLanguage

Somatic-ShortV @ NCI-Gadi
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/122
creator: https://workflowhub.eu/people/141
creator: https://workflowhub.eu/people/258
date created: 2021-08-18T23:14:55Z
date modified: 2025-07-25T02:06:02Z
description: Somatic-ShortV @ NCI-Gadi is a variant calling pipeline that calls somatic short variants (SNPs and indels) from tumour and matched normal BAM files following [GATK's Best Practice Workflow](https://gatk.broadinstitute.org/hc/en-us/articles/360035894731-Somatic-short-variant-discovery-SNVs-Indels-). This workflow is designed for the National Computational Infrastructure's (NCI) Gadi supercompter, leveraging multiple nodes on NCI Gadi to run all stages of the workflow in parallel. Infrastructure\_deployment\_metadata: Gadi (NCI)
is part of: https://workflowhub.eu/collections/6
keywords: GATK4, SNPs, INDELs, Somatic, variant_calling, Mutect2, NCI, NCI Gadi, Gadi, cancer, tumour, NCI-Gadi, scalable, VCF
license: GPL-3.0
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Cali Willet
type: Person

Shell Script
type: ComputerLanguage

Flashlite-Trinity
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/122
creator: https://workflowhub.eu/people/123
creator: https://workflowhub.eu/people/141
creator: Cali Willet
date created: 2021-08-18T23:17:42Z
date modified: 2023-01-16T13:51:45Z
description: Flashlite-Trinity contains two workflows that run Trinity on the [University of Queensland's HPC, Flashlite](https://rcc.uq.edu.au/flashlite). Trinity performs de novo transcriptome assembly of RNA-seq data by combining three independent software modules Inchworm, Chrysalis and Butterfly to process RNA-seq reads. The algorithm can detect isoforms, handle paired-end reads, multiple insert sizes and strandedness. Users can run Flashlite-Trinity on single samples, or smaller samples requiring <500Gb of memory or staged Trinity which is recommended for global assemblies with multiple sample inputs. Both implementations make use of Singularity containers to install software. Infrastructure\_deployment\_metadata: FlashLite (QRISCloud)
is part of: https://workflowhub.eu/collections/6
keywords: trinity, Transcriptomics, Assembly, illumina, salmon, scalable, global assemblies, rna-seq, de novo, transcriptome, strandedness, rna, singularity, container, PBS
license: GPL-3.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Cheminformatics - Docking
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Björn Grüning
creator: Frank von Delft
creator: Gianmauro Cuccuru
creator: Jack Scantlebury
creator: Rachael Skyner
creator: Simon Bray
creator: Susan Leung
creator: Tim Dudgeon
date created: 2020-04-10T14:32:01Z
date modified: 2023-01-16T13:40:50Z
description: Docking performed by rDock using as 3 different kind of inputs. More info can be found at https://covid19.galaxyproject.org/cheminformatics/
input: 0_Input Dataset
input: 1_Input Dataset
input: 2_Input Dataset Collection
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Björn Grüning
type: Person

Frank von Delft
type: Person

Gianmauro Cuccuru
type: Person

Jack Scantlebury
type: Person

Rachael Skyner
type: Person

Simon Bray
type: Person

Susan Leung
type: Person

Tim Dudgeon
type: Person

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conforms to: 1.0-RELEASE

1_Input Dataset
type: FormalParameter
conforms to: 1.0-RELEASE

Galaxy
type: ComputerLanguage

Nathaniel Butterworth
type: Person

Shell Script
type: ComputerLanguage

Flashlite-Juicer
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/122
creator: https://workflowhub.eu/people/141
creator: Nathaniel Butterworth
date created: 2021-08-18T23:19:40Z
date modified: 2023-01-16T13:51:45Z
description: Flashlite-Juicer is a PBS implementation of [Juicer](https://github.com/aidenlab/juicer) for University of Queensland's Flashlite HPC. Infrastructure\_deployment\_metadata: FlashLite (QRISCloud)
is part of: https://workflowhub.eu/collections/6
keywords: Juicer, Flashlite, Hi-C, PBS, TAD, scalable, map, FASTQ, BWA, topologically associating domains
license: GPL-3.0
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Shell Script
type: ComputerLanguage

Flashlite-Supernova
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-08-18T23:21:08Z
date modified: 2023-01-16T13:51:45Z
description: The Flashlite-Supernova pipeline runs Supernova to generate phased whole-genome de novo assemblies from a Chromium prepared library on [University of Queensland's HPC, Flashlite](https://rcc.uq.edu.au/flashlite). Infrastructure\_deployment\_metadata: FlashLite (QRISCloud)
is part of: https://workflowhub.eu/collections/6
keywords: Flashlite, Supernova, 10X, TELLSeq
license: GPL-3.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Cali Willet
type: Person

Shell Script
type: ComputerLanguage

RNASeq-DE @ NCI-Gadi
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/122
creator: https://workflowhub.eu/people/141
creator: Cali Willet
date created: 2021-08-18T23:24:08Z
date modified: 2023-01-16T13:51:46Z
description: RNASeq-DE @ NCI-Gadi processes RNA sequencing data (single, paired and/or multiplexed) for differential expression (raw FASTQ to counts). This pipeline consists of multiple stages and is designed for the National Computational Infrastructure's (NCI) Gadi supercompter, leveraging multiple nodes to run each stage in parallel. Infrastructure\_deployment\_metadata: Gadi (NCI)
is part of: https://workflowhub.eu/collections/6
keywords: RNASEQ, differential_expression, DE, Gadi, NCI, illumina, STAR, SAMTools, RSeQC, HTSeq, MultiQC, FastQC, BBduk, rna, expression, differential expression, FASTQ, counts, NCI-Gadi, rna-seq, workflow, bash, PBS, parallel, scalable
license: GPL-3.0
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Shell Script
type: ComputerLanguage

Bootstrapping-for-BQSR @ NCI-Gadi
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/141
creator: https://workflowhub.eu/people/258
date created: 2021-08-18T23:26:00Z
date modified: 2025-07-25T02:02:50Z
description: Bootstrapping-for-BQSR @ NCI-Gadi is a pipeline for bootstrapping a variant resource to enable GATK base quality score recalibration (BQSR) for non-model organisms that lack a publicly available variant resource. This implementation is optimised for the National Compute Infrastucture's Gadi HPC. Multiple rounds of bootstrapping can be performed. Users can use [Fastq-to-bam @ NCI-Gadi](https://workflowhub.eu/workflows/146) and [Germline-ShortV @ NCI-Gadi](https://workflowhub.eu/workflows/143) to produce required input files for Bootstrapping-for-BQSR @ NCI-Gadi. Infrastructure\_deployment\_metadata: Gadi (NCI)
is part of: https://workflowhub.eu/collections/6
keywords: GATK4, Bootstrapping, BQSR, SNPs, INDELs, illumina, WGS, Genomics, Alignment, variant calling, model, non-model, scalable, DNA, NCI, NCI-Gadi, PBS
license: GPL-3.0
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

Reverse read length
type: FormalParameter
conforms to: 1.0-RELEASE

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conforms to: 1.0-RELEASE

forward reads
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conforms to: 1.0-RELEASE

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conforms to: 1.0-RELEASE

Metadata file
type: FormalParameter
conforms to: 1.0-RELEASE

Primers are removed
type: FormalParameter
conforms to: 1.0-RELEASE

Reference database
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Reverse read length
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conforms to: 1.0-RELEASE

Reverse primer
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conforms to: 1.0-RELEASE

reverse reads
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conforms to: 1.0-RELEASE

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conforms to: 1.0-RELEASE

number of threads
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conforms to: 1.0-RELEASE

files_to_folder_fastqc
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conforms to: 1.0-RELEASE

files_to_folder_ngtax
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conforms to: 1.0-RELEASE

files_to_folder_phyloseq
type: FormalParameter
conforms to: 1.0-RELEASE

files_to_folder_picrust2
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conforms to: 1.0-RELEASE

turtle
type: FormalParameter
conforms to: 1.0-RELEASE

Quality assessment, amplicon classification and functional prediction
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/46
creator: https://workflowhub.eu/people/84
date created: 2021-09-30T11:15:19Z
date modified: 2025-09-11T07:04:34Z
description: Workflow for quality assessment of paired reads and classification using NGTax 2.0 and functional annotation using picrust2. In addition files are exported to their respective subfolders for easier data management in a later stage. Steps: - Quality plots (FastQC) - NG-TAX 2 High-throughput Amplicon Analysis - PICRUSt 2 - Function prediction from marker gene sequences - Export module for ngtax
input: Reverse read length
input: Forward primer
input: forward reads
input: Subfragment name
input: Metadata file
input: Primers are removed
input: Reference database
input: Reverse primer
input: reverse reads
input: Sample name
input: number of threads
is based on: https://workflowhub.eu/workflows/154
keywords: Amplicon, Classification, CWL
license: AFL-3.0
output: files_to_folder_fastqc
output: files_to_folder_ngtax
output: files_to_folder_phyloseq
output: files_to_folder_picrust2
output: turtle
producer: https://workflowhub.eu/projects/16
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 2

Galaxy
type: ComputerLanguage

Minimum quality score to call base
type: FormalParameter
conforms to: 1.0-RELEASE

Paired read collection for samples
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conforms to: 1.0-RELEASE

Primer BED
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conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

Reference FASTA
type: FormalParameter
conforms to: 1.0-RELEASE

Version of pangolin-data to use
type: FormalParameter
conforms to: 1.0-RELEASE

all_samples_nextclade
type: FormalParameter
conforms to: 1.0-RELEASE

all_samples_pangolin
type: FormalParameter
conforms to: 1.0-RELEASE

bamqc_report_html
type: FormalParameter
conforms to: 1.0-RELEASE

combined_multifasta
type: FormalParameter
conforms to: 1.0-RELEASE

ivar_consensus_genome
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conforms to: 1.0-RELEASE

ivar_variants_tabular
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conforms to: 1.0-RELEASE

primer_trimmed_bam
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conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

sars-cov-2-pe-illumina-artic-ivar-analysis/SARS-COV-2-ILLUMINA-AMPLICON-IVAR-PANGOLIN-NEXTCLADE
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Peter van Heusden
date created: 2026-03-10T03:01:54Z
date modified: 2026-03-10T03:01:54Z
description: Find and annotate variants in ampliconic SARS-CoV-2 Illumina sequencing data and classify samples with pangolin and nextclade
input: Minimum quality score to call base
input: Paired read collection for samples
input: Primer BED
input: Read fraction to call variant
input: Reference FASTA
input: Version of pangolin-data to use
is based on: https://workflowhub.eu/workflows/155
keywords: Virology
license: MIT
output: all_samples_nextclade
output: all_samples_pangolin
output: bamqc_report_html
output: combined_multifasta
output: ivar_consensus_genome
output: ivar_variants_tabular
output: primer_trimmed_bam
output: snpeff_annotated_vcf
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 7

Laure Quintric
type: Person

Nextflow
type: ComputerLanguage

SAMBA: Standardized and Automated MetaBarcoding Analyses workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/145
creator: https://workflowhub.eu/people/146
creator: https://workflowhub.eu/people/147
creator: https://workflowhub.eu/people/148
creator: Laure Quintric
date created: 2021-09-10T13:40:28Z
date modified: 2025-06-05T14:46:14Z
description: SAMBA is a FAIR scalable workflow integrating, into a unique tool, state-of-the-art bioinformatics and statistical methods to conduct reproducible eDNA analyses using Nextflow. SAMBA starts processing by verifying integrity of raw reads and metadata. Then all bioinformatics processing is done using commonly used procedure (QIIME 2 and DADA2) but adds new steps relying on dbOTU3 and microDecon to build high quality ASV count tables. Extended statistical analyses are also performed. Finally, SAMBA produces a full dynamic HTML report including resources used, commands executed, intermediate results, statistical analyses and figures. The SAMBA pipeline can run tasks across multiple compute infrastructures in a very portable manner. It comes with singularity containers making installation trivial and results highly reproducible.
keywords: Metabarcoding, Nextflow, 16S, 18S, eDNA
license: AGPL-3.0
producer: https://workflowhub.eu/projects/47
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Cheminformatics - SuCOS scoring
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Björn Grüning
creator: Frank von Delft
creator: Gianmauro Cuccuru
creator: Jack Scantlebury
creator: Rachael Skyner
creator: Simon Bray
creator: Susan Leung
creator: Tim Dudgeon
date created: 2020-04-10T14:50:31Z
date modified: 2023-01-16T13:40:57Z
description: This workflow generates binding scores that correlate well with binding affinities using an additional tool SuCOS Max, developed at Oxford University. More info can be found at https://covid19.galaxyproject.org/cheminformatics/
input: 0_Input Dataset Collection
input: 1_Input Dataset
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Björn Grüning
type: Person

Frank von Delft
type: Person

Gianmauro Cuccuru
type: Person

Jack Scantlebury
type: Person

Rachael Skyner
type: Person

Simon Bray
type: Person

Susan Leung
type: Person

Tim Dudgeon
type: Person

0_Input Dataset
type: FormalParameter
conforms to: 1.0-RELEASE

1_Input Dataset Collection
type: FormalParameter
conforms to: 1.0-RELEASE

Galaxy
type: ComputerLanguage

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

Cryo electron microscopy of SARS-CoV-2 spike in prefusion state
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-09-14T13:52:01Z
date modified: 2023-01-16T13:52:18Z
description: Continuous flexibility analysis of SARS-CoV-2 Spike prefusion structures
license: Apache-2.0
producer: https://workflowhub.eu/projects/26
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Cheminformatics - TransFS scoring
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Björn Grüning
creator: Frank von Delft
creator: Gianmauro Cuccuru
creator: Jack Scantlebury
creator: Rachael Skyner
creator: Simon Bray
creator: Susan Leung
creator: Tim Dudgeon
date created: 2020-04-10T14:51:59Z
date modified: 2023-01-16T13:41:04Z
description: This workflow generates binding scores that correlate well with binding affinities using an additional tool TransFS, developed at Oxford University. More info can be found at https://covid19.galaxyproject.org/cheminformatics/
input: 0_Input Dataset
input: 1_Input Dataset Collection
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Björn Grüning
type: Person

Frank von Delft
type: Person

Gianmauro Cuccuru
type: Person

Jack Scantlebury
type: Person

Rachael Skyner
type: Person

Simon Bray
type: Person

Susan Leung
type: Person

Tim Dudgeon
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conforms to: 1.0-RELEASE

1_Input Parameter
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conforms to: 1.0-RELEASE

2_Input Dataset
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conforms to: 1.0-RELEASE

Galaxy
type: ComputerLanguage

Cheminformatics - Filter results
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Björn Grüning
creator: Frank von Delft
creator: Gianmauro Cuccuru
creator: Jack Scantlebury
creator: Rachael Skyner
creator: Simon Bray
creator: Susan Leung
creator: Tim Dudgeon
date created: 2020-04-10T14:56:44Z
date modified: 2023-01-16T13:41:12Z
description: This workflow combines SDF files from all fragments into a single dataset and filters to include only the lowest (best) scoring pose for each compound. This file of optimal poses for all ligands is used to compare to a database of Enamine and Chemspace compounds to select the best scoring 500 matches. More info can be found at https://covid19.galaxyproject.org/cheminformatics/
input: 0_Input Dataset Collection
input: 1_Input Parameter
input: 2_Input Dataset
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Björn Grüning
type: Person

Frank von Delft
type: Person

Gianmauro Cuccuru
type: Person

Jack Scantlebury
type: Person

Rachael Skyner
type: Person

Simon Bray
type: Person

Susan Leung
type: Person

Tim Dudgeon
type: Person

0_Input Dataset
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conforms to: 1.0-RELEASE

1_Input Dataset
type: FormalParameter
conforms to: 1.0-RELEASE

2_Input Dataset Collection
type: FormalParameter
conforms to: 1.0-RELEASE

3_Input Dataset
type: FormalParameter
conforms to: 1.0-RELEASE

Galaxy
type: ComputerLanguage

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

testEntryTitleNew
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-09-15T13:56:31Z
date modified: 2023-01-16T13:52:41Z
description: Cryo-EM processing workflow
license: Apache-2.0
producer: https://workflowhub.eu/projects/26
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

entryTitleTest
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-09-15T15:26:44Z
date modified: 2023-01-16T13:52:45Z
description: Cryo-EM processing workflow
license: Apache-2.0
producer: https://workflowhub.eu/projects/26
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Input Gene List
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conforms to: 1.0-RELEASE

GO Enriched Terms
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conforms to: 1.0-RELEASE

GO Gene Prioritization
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conforms to: 1.0-RELEASE

GO Heatmap
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conforms to: 1.0-RELEASE

Galaxy
type: ComputerLanguage

BioTranslator Workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-09-24T15:16:59Z
date modified: 2023-01-16T13:52:49Z
description: BioTranslator performs sequentially pathway analysis and gene prioritization: A specific operator is executed for each task to translate the input gene set into semantic terms and pinpoint the pivotal-role genes on the derived semantic network. The output consists of the set of statistically significant semantic terms and the associated hub genes (the gene signature), prioritized according to their involvement in the underlying semantic topology.
input: Input Gene List
is based on: https://workflowhub.eu/workflows/189
keywords: Semantic Network Analysis, Gene Prioritization, Pathway Analysis, Biomedical Ontologies, Semantic Interpretation
license: Apache-2.0
output: GO Enriched Terms
output: GO Gene Prioritization
output: GO Heatmap
producer: https://workflowhub.eu/projects/58
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 3

Cheminformatics - XChem combined
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Björn Grüning
creator: Frank von Delft
creator: Gianmauro Cuccuru
creator: Jack Scantlebury
creator: Rachael Skyner
creator: Simon Bray
creator: Susan Leung
creator: Tim Dudgeon
date created: 2020-04-10T15:02:39Z
date modified: 2023-01-16T13:41:21Z
description: This workflow is used for the virtual screening of the SARS-CoV-2 main protease (de.NBI-cloud, STFC). It includes Charge enumeration, Generation of 3D conformations, Preparation of active site for docking using rDock, Docking, Scoring and Selection of compounds available. More info can be found at https://covid19.galaxyproject.org/cheminformatics/
input: 0_Input Dataset
input: 1_Input Dataset
input: 2_Input Dataset Collection
input: 3_Input Dataset
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Sarai Varona and Miguel Juliá and Sara Monzon and Alexander Peltzer and Alison Meynert and Edgar Garriga Nogales and Erik Garrison and Gisela Gabernet and Harshil Patel and Joao Curado and Jose Espinosa-Carrasco and Katrin Sameith and Marta Pozuelo and Maxime Garcia and Michael Heuer and Phil Ewels and Simon Heumos and Stephen Kelly and Thanh Le Viet and Isabel Cuesta
type: Person

Nextflow
type: ComputerLanguage

Sophie Alain
type: Person

Nextflow
type: ComputerLanguage

ASPICov
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/146
creator: https://workflowhub.eu/people/148
creator: https://workflowhub.eu/people/150
creator: Valentin Tilloy
creator: Sophie Alain
date created: 2021-09-24T14:22:51Z
date modified: 2023-01-16T13:52:59Z
description: ASPICov was developed to provide a rapid, reliable and complete analysis of NGS SARS-Cov2 samples to the biologist. This broad application tool allows to process samples from either capture or amplicon strategy and Illumina or Ion Torrent technology. To ensure FAIR data analysis, this Nextflow pipeline follows nf-core guidelines and use Singularity containers. Availability and Implementation: https://gitlab.com/vtilloy/aspicov Citation: Valentin Tilloy, Pierre Cuzin, Laura Leroi, Emilie Guérin, Patrick Durand, Sophie Alain ASPICov: An automated pipeline for identification of SARS-Cov2 nucleotidic variants PLoS One 2022 Jan 26;17(1):e0262953: https://pubmed.ncbi.nlm.nih.gov/35081137/
keywords: covid-19
license: Apache-2.0
producer: CHU Limoges - UF9481 Bioinformatique / CNR Herpesvirus
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

Input sets of genes
type: FormalParameter
conforms to: 1.0-RELEASE

Distances Matrix
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conforms to: 1.0-RELEASE

Heatmap
type: FormalParameter
conforms to: 1.0-RELEASE

Workflow of BioTranslator Comparative Analysis
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-09-26T18:20:05Z
date modified: 2023-01-16T13:53:00Z
description: This workflow is based on the idea of comparing different gene sets through their semantic interpretation. In many cases, the user studies a specific phenotype (e.g. disease) by analyzing lists of genes resulting from different samples or patients. Their pathway analysis could result in different semantic networks, revealing mechanistic and phenotypic divergence between these gene sets. The workflow of BioTranslator Comparative Analysis compares quantitatively the outputs of pathway analysis, based on the topology of the underlying ontological graph, in order to derive a semantic similarity value for each pair of the initial gene sets. The workflow is available in a Galaxy application and can be used for 14 species. The algorithm accepts as input a batch of gene sets, such as BioTranslator, for the same organism. It performs pathway analysis according to the user-selected ontology and then it compares the derived semantic networks and extracts a matrix with their distances, as well as a respective heatmap.
input: Input sets of genes
keywords: Semantic Network Analysis, Semantic Comparison, Pathway Analysis
license: Apache-2.0
output: Distances Matrix
output: Heatmap
producer: https://workflowhub.eu/projects/58
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

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Protein MD Setup tutorial using BioExcel Building Blocks (biobb) in Galaxy
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date created: 2021-09-26T19:50:02Z
date modified: 2023-01-16T13:53:08Z
description: Galaxy workflow example that illustrate the process of setting up a simulation system containing a protein, step by step, using the [BioExcel Building Blocks](/projects/11) library (biobb). The particular example used is the Lysozyme protein (PDB code 1AKI). This workflow returns a resulting protein structure and simulated 3D trajectories. Designed for running on the https://dev.usegalaxy.es Galaxy instance.
is part of: https://workflowhub.eu/collections/3
license: Apache-2.0
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output: mypdb2gmx.zip
output: mysolvate.gro
output: mysolvate.zip
output: rmsd_exp.xvg
output: rmsd_first.xvg
producer: https://workflowhub.eu/projects/11
programming language: Galaxy
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Jupyter Notebook Structural DNA helical parameters tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T14:00:17Z
date modified: 2026-03-26T14:03:37Z
description: # Structural DNA helical parameters from MD trajectory tutorial using BioExcel Building Blocks (biobb) **Based on the [NAFlex](https://mmb.irbbarcelona.org/NAFlex) server and in particular in its [Nucleic Acids Analysis section](https://mmb.irbbarcelona.org/NAFlex/help.php?id=tutorialAnalysisNA).** *** This tutorial aims to illustrate the process of **extracting structural and dynamical properties** from a **DNA MD trajectory helical parameters**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Drew Dickerson Dodecamer** sequence -CGCGAATTCGCG- (PDB code [1BNA](https://www.rcsb.org/structure/1BNA)). The trajectory used is a 500ns-long MD simulation taken from the [BigNASim](https://mmb.irbbarcelona.org/BIGNASim/) database ([NAFlex_DDD_II](https://mmb.irbbarcelona.org/BIGNASim/getStruc.php?idCode=NAFlex_DDD_II) entry). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/195
is part of: https://workflowhub.eu/collections/19
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 7

Jupyter
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Jupyter Notebook ABC MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T11:46:01Z
date modified: 2026-03-26T11:48:52Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/196
is part of: https://workflowhub.eu/collections/19
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Jupyter
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version: 7

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lncRNA
type: ComputationalWorkflow
type: SoftwareSourceCode
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date created: 2021-09-29T08:25:31Z
date modified: 2023-01-16T13:53:17Z
description: Analysis of RNA-seq data starting from BAM and focusing on mRNA, lncRNA and miRNA
license: no-permission
producer: https://workflowhub.eu/projects/58
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

nf-core/viralrecon
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Sarai Varona and Miguel Juliá and Sara Monzon and Alexander Peltzer and Alison Meynert and Edgar Garriga Nogales and Erik Garrison and Gisela Gabernet and Harshil Patel and Joao Curado and Jose Espinosa-Carrasco and Katrin Sameith and Marta Pozuelo and Maxime Garcia and Michael Heuer and Phil Ewels and Simon Heumos and Stephen Kelly and Thanh Le Viet and Isabel Cuesta
date created: 2020-05-14T14:10:58Z
date modified: 2023-01-16T13:41:25Z
description: nfcore/viralrecon is a bioinformatics analysis pipeline used to perform assembly and intrahost/low-frequency variant calling for viral samples. The pipeline currently supports metagenomics and amplicon sequencing data derived from the Illumina sequencing platform. This pipeline is a re-implementation of the SARS_Cov2_consensus-nf and SARS_Cov2_assembly-nf pipelines initially developed by Sarai Varona and Sara Monzon from BU-ISCIII. Porting both of these pipelines to nf-core was an international collaboration between numerous contributors and developers, led by Harshil Patel from the The Bioinformatics & Biostatistics Group at The Francis Crick Institute, London. We appreciated the need to have a portable, reproducible and scalable pipeline for the analysis of COVID-19 sequencing samples and so the Avengers Assembled! Please come and join us and add yourself to the contributor list :) We have integrated a number of options in the pipeline to allow you to run specific aspects of the workflow if you so wish. For example, you can skip all of the assembly steps with the --skip_assembly parameter. See usage docs for all of the available options when running the pipeline. Please click here to see an example MultiQC report generated using the parameters defined in this configuration file to run the pipeline on samples which were prepared from the ncov-2019 ARTIC Network V1 amplicon set and sequenced on the Illumina MiSeq platform in 301bp paired-end format. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. Furthermore, automated continuous integration tests to run the pipeline on a full-sized dataset are passing on AWS cloud. Pipeline summary Download samples via SRA, ENA or GEO ids (ENA FTP, parallel-fastq-dump; if required) Merge re-sequenced FastQ files (cat; if required) Read QC (FastQC) Adapter …
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/5
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Andreas Wilm and October SESSIONS and Paola Florez DE SESSIONS and ZHU Yuan and Shuzhen SIM and CHU Wenhan Collins
type: Person

Nextflow
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COMPSs
type: ComputerLanguage

Protein MD Setup HPC tutorial using BioExcel Building Blocks (biobb) in PyCOMPSs
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Pau Andrio
creator: https://workflowhub.eu/people/57
date created: 2021-09-29T08:26:39Z
date modified: 2023-01-16T13:53:23Z
description: This PyCOMPSs workflow tutorial aims to illustrate the process of setting up a simulation system containing a protein, step by step, using the BioExcel Building Blocks library (biobb) in PyCOMPSs for execution on HPC. Three variants of the MD Setup workflows are included, supporting a list of structures, a list of mutations, or a cumulative set of mutations.
is part of: https://workflowhub.eu/collections/3
keywords: molecular dynamics, GROMACS, BioBB
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: COMPSs
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Protein MD Setup tutorial using BioExcel Building Blocks (biobb) in KNIME
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date created: 2021-09-29T14:25:42Z
date modified: 2022-04-11T09:27:55Z
description: This is an experimental KNIME workflow of using the BioExcel building blocks to implement the Protein MD Setup tutorial for molecular dynamics with GROMACS. Note that this workflow won't import in KNIME without the [experimental KNIME nodes](https://bioexcel.eu/research/projects/biobb_knime/) for BioBB - contact Adam Hospital for details.
encoding format: application/zip
is part of: https://workflowhub.eu/collections/3
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: KNIME
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version: 1

nf-core/vipr
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Andreas Wilm and October SESSIONS and Paola Florez DE SESSIONS and ZHU Yuan and Shuzhen SIM and CHU Wenhan Collins
date created: 2020-05-14T14:42:23Z
date modified: 2023-01-16T13:41:25Z
description: nf-core/vipr is a bioinformatics best-practice analysis pipeline for assembly and intrahost / low-frequency variant calling for viral samples. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible. Pipeline Steps Step Main program/s Trimming, combining of read-pairs per sample and QC Skewer, FastQC Decontamination decont Metagenomics classification / Sample purity Kraken Assembly to contigs BBtools’ Tadpole Assembly polishing ViPR Tools Mapping to assembly BWA, LoFreq Low frequency variant calling LoFreq Coverage and variant AF plots (two processes) Bedtools, ViPR Tools Documentation Documentation about the pipeline can be found in the docs/ directory: Installation and configuration Running the pipeline Output and how to interpret the results Credits This pipeline was originally developed by Andreas Wilm (andreas-wilm) at Genome Institute of Singapore. It started out as an ecosystem around LoFreq and went through a couple of iterations. The current version had three predecessors ViPR 1, ViPR 2 and ViPR 3. An incomplete list of publications using (previous versions of) ViPR: Sessions et. al., PLoS Negl Trop Dis., 2015 Sim et al., PLoS Negl Trop Dis., 2015 Plenty of people provided essential feedback, including: October SESSIONS Paola Florez DE SESSIONS ZHU Yuan Shuzhen SIM CHU Wenhan Collins
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/5
programming language: Nextflow
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version: 1

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Preparing a data set for Deep Learning from zipped ABR raw data files
type: ComputationalWorkflow
type: SoftwareSourceCode
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creator: Elida Schneltzer
date created: 2021-10-19T10:47:54Z
date modified: 2023-01-16T13:53:24Z
description: This notebook is about pre-processing the Auditory Brainstem Response (ABR) raw data files provided by [Ingham et. al](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000194) to create a data set for Deep Learning models. The unprocessed ABR data files are available at [Dryad](https://datadryad.org/stash/dataset/doi:10.5061/dryad.cv803rv). Since the ABR raw data are available as zip-archives, these have to be unzipped and the extracted raw data files parsed so that the time series corresponding to the ABR audiograms can be saved in a single csv file. The final data set contains the ABR time series, an individual mouse identifier, stimulus frequency, stimulus sound pressure level (SPL) and a manually determined hearing threshold. For each mouse there are different time series corresponding to six different sound stimuli: broadband click, 6, 12, 18, 24, and 30 kHz, each of which was measured for a range of sound pressure levels. The exact range of sound levels can vary between the different mice and stimuli. The following is done: * The zip archives are unpacked. * The extracted ABR raw data files are parsed and collected in one csv file per archive. * The csv files are merged into a data set of time series. Each time series corresponds to an ABR audiogram measured for a mouse at a specific frequency and sound level. * The mouse phenotyping data are available in Excel format. The individual data sheets are combined into one mouse phenotyping data set, maintaining the mouse pipeline and the cohort type mapping. In addition, the hearing thresholds are added to the ABR audiogram data set. * The data sets are curated: * there is a single curve per mouse, stimulus frequency and sound level, * each sound level is included in the list of potential sound pressure levels, * for each mouse for which an ABR audiogram has been measured, mouse phenotyping data are also provided.
keywords: ABR, DL
license: Apache-2.0
producer: https://workflowhub.eu/projects/67
programming language: Jupyter
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version: 1

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creator: https://workflowhub.eu/people/139
date created: 2022-10-17T02:51:00Z
date modified: 2023-01-30T18:21:31Z
description: # BAM-to-FASTQ-QC ## General recommendations for using BAM-to-FASTQ-QC Please see the [`Genome assembly with hifiasm on Galaxy Australia`](https://australianbiocommons.github.io/how-to-guides/genome_assembly/hifi_assembly) guide. ## Acknowledgements The workflow & the [doc_guidelines template used](https://github.com/AustralianBioCommons/doc_guidelines) are supported by the Australian BioCommons via Bioplatforms Australia funding, the Australian Research Data Commons (https://doi.org/10.47486/PL105) and the Queensland Government RICF programme. Bioplatforms Australia and the Australian Research Data Commons are enabled by the National Collaborative Research Infrastructure Strategy (NCRIS).
input: Input BAM
is based on: https://workflowhub.eu/workflows/220
is part of: https://workflowhub.eu/collections/5
is part of: https://workflowhub.eu/collections/6
keywords: BAM, FASTQ, Conversion, QC
license: GPL-3.0
output: BAM file information
output: Converted FastQ reads
output: FastQC HTML
output: FastQC text file
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
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PacBio HiFi genome assembly using hifiasm v2.1
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creator: Katherine Farquharson
date created: 2022-10-21T05:13:12Z
date modified: 2023-01-30T18:21:31Z
description: # PacBio HiFi genome assembly using hifiasm v2.1 ## General usage recommendations Please see the [Genome assembly with hifiasm on Galaxy Australia](https://australianbiocommons.github.io/how-to-guides/genome_assembly/hifi_assembly) guide. ## See [change log](./change_log.md) ## Acknowledgements The workflow & the [doc_guidelines template used](https://github.com/AustralianBioCommons/doc_guidelines) are supported by the Australian BioCommons via Bioplatforms Australia funding, the Australian Research Data Commons (https://doi.org/10.47486/PL105) and the Queensland Government RICF programme. Bioplatforms Australia and the Australian Research Data Commons are enabled by the National Collaborative Research Infrastructure Strategy (NCRIS).
input: FASTQ input
is based on: https://workflowhub.eu/workflows/221
is part of: https://workflowhub.eu/collections/5
is part of: https://workflowhub.eu/collections/6
keywords: FASTQ, hifiasm, HiFi, genome_assembly
license: GPL-3.0
output: HiFi Adapter Filter on input dataset(s): blocklist
output: HiFi Adapter Filter on input dataset(s): clean reads
output: HiFi Adapter Filter on input dataset(s): contaminant blastout
output: HiFi Adapter Filter on input dataset(s): contaminant statistic
output: converted FASTA
output: metrics
output: primary bandage image
output: primary bandage info
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 3

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date created: 2021-11-08T04:34:47Z
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description: Data QC step, can run alone or as part of a combined workflow for large genome assembly. * What it does: Reports statistics from sequencing reads. * Inputs: long reads (fastq.gz format), short reads (R1 and R2) (fastq.gz format). * Outputs: For long reads: a nanoplot report (the HTML report summarizes all the information). For short reads: a MultiQC report. * Tools used: Nanoplot, FastQC, MultiQC. * Input parameters: None required. * Workflow steps: Long reads are analysed by Nanoplot; Short reads (R1 and R2) are analysed by FastQC; the resulting reports are processed by MultiQC. * Options: see the tool settings options at runtime and change as required. Alternative tool option: fastp Infrastructure_deployment_metadata: Galaxy Australia (Galaxy)
input: Input file: Illumina reads R1
input: Input file: Illumina reads R2
input: Input file: long reads
is part of: https://workflowhub.eu/collections/6
keywords: Large-genome-assembly
license: Apache-2.0
output: MultiQC on input dataset(s): Webpage
output: NanoPlot on input dataset(s): HTML report
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
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kmer counting - meryl
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description: Kmer counting step, can run alone or as part of a combined workflow for large genome assembly. * What it does: Estimates genome size and heterozygosity based on counts of kmers * Inputs: One set of short reads: e.g. R1.fq.gz * Outputs: GenomeScope graphs * Tools used: Meryl, GenomeScope * Input parameters: None required * Workflow steps: The tool meryl counts kmers in the input reads (k=21), then converts this into a histogram. GenomeScope: runs a model on the histogram; reports estimates. k-mer size set to 21. * Options: Use a different kmer counting tool. e.g. khmer. Infrastructure_deployment_metadata: Galaxy Australia (Galaxy)
input: Illumina reads R1
is part of: https://workflowhub.eu/collections/6
keywords: Large-genome-assembly
license: Apache-2.0
output: GenomeScope on input dataset(s) Linear plot
output: GenomeScope on input dataset(s) Log plot
output: GenomeScope on input dataset(s) Transformed linear plot
output: GenomeScope on input dataset(s) Transformed log plot
output: Meryl on input dataset(s): read-db.meryldb
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Trim and filter reads - fastp
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date created: 2021-11-08T04:56:09Z
date modified: 2023-01-30T18:21:31Z
description: Trim and filter reads; can run alone or as part of a combined workflow for large genome assembly. * What it does: Trims and filters raw sequence reads according to specified settings. * Inputs: Long reads (format fastq); Short reads R1 and R2 (format fastq) * Outputs: Trimmed and filtered reads: fastp_filtered_long_reads.fastq.gz (But note: no trimming or filtering is on by default), fastp_filtered_R1.fastq.gz, fastp_filtered_R2.fastq.gz * Reports: fastp report on long reads, html; fastp report on short reads, html * Tools used: fastp (Note. The latest version (0.20.1) of fastp has an issue displaying plot results. Using version 0.19.5 here instead until this is rectified). * Input parameters: None required, but recommend removing the long reads from the workflow if not using any trimming/filtering settings. Workflow steps: Long reads: fastp settings: * These settings have been changed from the defaults (so that all filtering and trimming settings are now disabled). * Adapter trimming options: Disable adapter trimming: yes * Filter options: Quality filtering options: Disable quality filtering: yes * Filter options: Length filtering options: Disable length filtering: yes * Read modification options: PolyG tail trimming: Disable * Output options: output JSON report: yes Short reads: fastp settings: * adapter trimming (default setting: adapters are auto-detected) * quality filtering (default: phred quality 15), unqualified bases limit (default = 40%), number of Ns allowed in a read (default = 5) * length filtering (default length = min 15) * polyG tail trimming (default = on for NextSeq/NovaSeq data which is auto detected) * Output options: output JSON report: yes Options: * Change any settings in fastp for any of the input reads. * Adapter trimming: input the actual adapter sequences. (Alternative tool for long read adapter trimming: Porechop.) * Trimming n bases from ends of reads if quality less than value x (Alternative tool for trimming long reads: NanoFilt.) * Di…
input: Illumina reads R1
input: Illumina reads R2
input: long reads
is part of: https://workflowhub.eu/collections/6
keywords: Large-genome-assembly
license: Apache-2.0
output: fastp filtered R1 reads
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output: fastp report on long reads html
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Assembly with Flye
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date created: 2021-11-08T05:07:16Z
date modified: 2023-01-30T18:21:31Z
description: Assembly with Flye; can run alone or as part of a combined workflow for large genome assembly. * What it does: Assembles long reads with the tool Flye * Inputs: long reads (may be raw, or filtered, and/or corrected); fastq.gz format * Outputs: Flye assembly fasta; Fasta stats on assembly.fasta; Assembly graph image from Bandage; Bar chart of contig sizes; Quast reports of genome assembly * Tools used: Flye, Fasta statistics, Bandage, Bar chart, Quast * Input parameters: None required, but recommend setting assembly mode to match input sequence type Workflow steps: * Long reads are assembled with Flye, using default tool settings. Note: the default setting for read type ("mode") is nanopore raw. Change this at runtime if required. * Statistics are computed from the assembly.fasta file output, using Fasta Statistics and Quast (is genome large: Yes; distinguish contigs with more that 50% unaligned bases: no) * The graphical fragment assembly file is visualized with the tool Bandage. * Assembly information sent to bar chart to visualize contig sizes Options * See other Flye options. * Use a different assembler (in a different workflow). * Bandage image options - change size (max size is 32767), labels - add (e.g. node lengths). You can also install Bandage on your own computer and donwload the "graphical fragment assembly" file to view in greater detail. Infrastructure_deployment_metadata: Galaxy Australia (Galaxy)
input: long reads
is part of: https://workflowhub.eu/collections/6
keywords: Large-genome-assembly
license: Apache-2.0
output: Bandage Image on input dataset(s): Assembly Graph Image
output: Bar chart showing contig sizes
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Assembly polishing
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date created: 2026-04-19T01:34:42Z
date modified: 2026-04-19T01:44:02Z
description: Assembly polishing; can run alone or as part of a combined workflow for large genome assembly. * What it does: Polishes (corrects) an assembly, using long reads (with the tools Racon and Medaka) and short reads (with the tool Racon). (Note: medaka is only for nanopore reads, not PacBio reads). * Inputs: assembly to be polished: assembly.fasta; long reads - the same set used in the assembly (e.g. may be raw or filtered) fastq.gz format; short reads, R1 only, in fastq.gz format * Outputs: Racon+Medaka+Racon polished_assembly. fasta; Fasta statistics after each polishing tool * Tools used: Minimap2, Racon, Fasta statistics, Medaka * Input parameters: None required. The Medaka model is set to r941_min_hac_g507 (R9.4.1 pore, MinION, Guppy 5 High Accuracy Calling). To use a different model, edit the workflow before importing. Workflow steps: -1- Polish with long reads: using Racon * Long reads and assembly contigs => Racon polishing (subworkflow): * minimap2 : long reads are mapped to assembly => overlaps.paf. * overaps, long reads, assembly => Racon => polished assembly 1 * using polished assembly 1 as input; repeat minimap2 + racon => polished assembly 2 * using polished assembly 2 as input, repeat minimap2 + racon => polished assembly 3 * using polished assembly 3 as input, repeat minimap2 + racon => polished assembly 4 * Racon long-read polished assembly => Fasta statistics * Note: The Racon tool panel can be a bit confusing and is under review for improvement. Presently it requires sequences (= long reads), overlaps (= the paf file created by minimap2), and target sequences (= the contigs to be polished) as per "usage" described here https://github.com/isovic/racon/blob/master/README.md * Note: Racon: the default setting for "output unpolished target sequences?" is No. This has been changed to Yes for all Racon steps in these polishing workflows. This means that even if no polishes are made in some contigs, they will be part of the output fasta file. * Note: the cont…
is based on: https://workflowhub.eu/workflows/226
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keywords: Large-genome-assembly
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description: Assembly polishing subworkflow: Racon polishing with long reads Inputs: long reads and assembly contigs Workflow steps: * minimap2 : long reads are mapped to assembly => overlaps.paf. * overaps, long reads, assembly => Racon => polished assembly 1 * using polished assembly 1 as input; repeat minimap2 + racon => polished assembly 2 * using polished assembly 2 as input, repeat minimap2 + racon => polished assembly 3 * using polished assembly 3 as input, repeat minimap2 + racon => polished assembly 4 * Racon long-read polished assembly => Fasta statistics * Note: The Racon tool panel can be a bit confusing and is under review for improvement. Presently it requires sequences (= long reads), overlaps (= the paf file created by minimap2), and target sequences (= the contigs to be polished) as per "usage" described here https://github.com/isovic/racon/blob/master/README.md * Note: Racon: the default setting for "output unpolished target sequences?" is No. This has been changed to Yes for all Racon steps in these polishing workflows. This means that even if no polishes are made in some contigs, they will be part of the output fasta file. * Note: the contigs output by Racon have new tags in their headers. For more on this see https://github.com/isovic/racon/issues/85. Infrastructure_deployment_metadata: Galaxy Australia (Galaxy)
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input: long reads
input: minimap setting (for long reads)
is part of: https://workflowhub.eu/collections/6
keywords: Large-genome-assembly
license: Apache-2.0
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description: Assembly polishing subworkflow: Racon polishing with short reads Inputs: short reads and assembly (usually pre-polished with other tools first, e.g. Racon + long reads; Medaka) Workflow steps: * minimap2: short reads (R1 only) are mapped to the assembly => overlaps.paf. Minimap2 setting is for short reads. * overlaps + short reads + assembly => Racon => polished assembly 1 * using polished assembly 1 as input; repeat minimap2 + racon => polished assembly 2 * Racon short-read polished assembly => Fasta statistics Infrastructure_deployment_metadata: Galaxy Australia (Galaxy)
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input: Illumina reads, R1, in fastq.gz format
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keywords: Large-genome-assembly
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Assess genome quality
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date created: 2021-11-08T06:03:05Z
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description: Assess genome quality; can run alone or as part of a combined workflow for large genome assembly. * What it does: Assesses the quality of the genome assembly: generate some statistics and determine if expected genes are present; align contigs to a reference genome. * Inputs: polished assembly; reference_genome.fasta (e.g. of a closely-related species, if available). * Outputs: Busco table of genes found; Quast HTML report, and link to Icarus contigs browser, showing contigs aligned to a reference genome * Tools used: Busco, Quast * Input parameters: None required Workflow steps: Polished assembly => Busco * First: predict genes in the assembly: using Metaeuk * Second: compare the set of predicted genes to the set of expected genes in a particular lineage. Default setting for lineage: Eukaryota Polished assembly and a reference genome => Quast * Contigs/scaffolds file: polished assembly * Type of assembly: Genome * Use a reference genome: Yes * Reference genome: Arabidopsis genome * Is the genome large (> 100Mbp)? Yes. * All other settings as defaults, except second last setting: Distinguish contigs with more than 50% unaligned bases as a separate group of contigs?: change to No Options Gene prediction: * Change tool used by Busco to predict genes in the assembly: instead of Metaeuk, use Augustus. * To do this: select: Use Augustus; Use another predefined species model; then choose from the drop down list. * Select from a database of trained species models. list here: https://github.com/Gaius-Augustus/Augustus/tree/master/config/species * Note: if using Augustus: it may fail if the input assembly is too small (e.g. a test-size data assembly). It can't do the training part properly. Compare genes found to other lineage: * Busco has databases of lineages and their expected genes. Option to change lineage. * Not all lineages are available - there is a mix of broader and narrower lineages. - list of lineages here: https://busco.ezlab.org/list_of_lineages.html. * To see t…
input: Polished assembly
input: Reference genome
is part of: https://workflowhub.eu/collections/6
keywords: Large-genome-assembly
license: Apache-2.0
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Pathway Analysis
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date created: 2020-05-29T10:01:25Z
date modified: 2023-01-16T13:41:28Z
description: Given a set of pathways generated by RetroPath2.0, this workflow informs the user as to the theoretically best performing ones based on four criteria: FBA, thermodynamic feasibility, length of the pathway, and reaction rule score.
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Combined workflows for large genome assembly
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date created: 2021-11-08T06:08:25Z
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description: Combined workflow for large genome assembly The tutorial document for this workflow is here: https://doi.org/10.5281/zenodo.5655813 What it does: A workflow for genome assembly, containing subworkflows: * Data QC * Kmer counting * Trim and filter reads * Assembly with Flye * Assembly polishing * Assess genome quality Inputs: * long reads and short reads in fastq format * reference genome for Quast Outputs: * Data information - QC, kmers * Filtered, trimmed reads * Genome assembly, assembly graph, stats * Polished assembly, stats * Quality metrics - Busco, Quast Options * Omit some steps - e.g. Data QC and kmer counting * Replace a module with one using a different tool - e.g. change assembly tool Infrastructure_deployment_metadata: Galaxy Australia (Galaxy)
is part of: https://workflowhub.eu/collections/6
keywords: Large-genome-assembly
license: Apache-2.0
producer: https://workflowhub.eu/projects/30
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description: MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data
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date created: 2021-11-11T03:37:33Z
date modified: 2024-05-13T02:08:43Z
description: A hecatomb is a great sacrifice or an extensive loss. Heactomb the software empowers an analyst to make data driven decisions to 'sacrifice' false-positive viral reads from metagenomes to enrich for true-positive viral reads. This process frequently results in a great loss of suspected viral sequences / contigs. For information about installation, usage, tutorial etc please refer to the documentation: https://hecatomb.readthedocs.io/en/latest/ ### Quick start guide Install Hecatomb from Bioconda ```bash # create an env called hecatomb and install Hecatomb in it conda create -n hecatomb -c conda-forge -c bioconda hecatomb # activate conda env conda activate hecatomb # check the installation hecatomb -h # download the databases - you only have to do this once hecatomb install # Run the test dataset hecatomb run --test ```
license: MIT
producer: HecatombDevelopment
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Michael Franklin; Jiaan Yu; Juny Kesumadewi
type: Person

Janis
type: ComputerLanguage

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Janis Germline Variant-Calling Workflow (GATK)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Richard Lupat
creator: Michael Franklin; Jiaan Yu; Juny Kesumadewi
date created: 2021-11-12T02:30:06Z
date modified: 2023-01-16T13:54:51Z
description: This is a genomics pipeline to do a single germline sample variant-calling, adapted from GATK Best Practice Workflow. This workflow is a reference pipeline for using the Janis Python framework (pipelines assistant). - Alignment: bwa-mem - Variant-Calling: GATK HaplotypeCaller - Outputs the final variants in the VCF format. **Resources** This pipeline has been tested using the HG38 reference set, available on Google Cloud Storage through: - https://console.cloud.google.com/storage/browser/genomics-public-data/references/hg38/v0/ This pipeline expects the assembly references to be as they appear in that storage (".fai", ".amb", ".ann", ".bwt", ".pac", ".sa", "^.dict"). The known sites (snps_dbsnp, snps_1000gp, known_indels, mills_indels) should be gzipped and tabix indexed. Infrastructure_deployment_metadata: Spartan (Unimelb)
input: align_and_sort_sortsam_tmpDir
input: cutadapt_adapters
input: fastqs
input: gatk_intervals
input: known_indels
input: mills_indels
input: reference
input: sample_name
input: snps_1000gp
input: snps_dbsnp
is part of: https://workflowhub.eu/collections/6
license: Apache-2.0
output: out_bam
output: out_fastqc_reports
output: out_performance_summary
output: out_variants_bamstats
output: out_variants_gatk
output: out_variants_gatk_split
producer: Janis
programming language: Janis
sd publisher: about.workflowhub.eu
version: 1

Gareth Price
type: Person

Galaxy
type: ComputerLanguage

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Purge duplicates from hifiasm assembly v1.0
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/139
creator: Gareth Price
date created: 2022-10-17T02:53:20Z
date modified: 2023-01-30T18:21:31Z
description: # Purge-duplicates-from-hifiasm-assembly ## General recommendations for using `Purge-duplicates-from-hifiasm-assembly` Please see the [`Genome assembly with hifiasm on Galaxy Australia`](https://australianbiocommons.github.io/how-to-guides/genome_assembly/hifi_assembly) guide. ## Acknowledgements The workflow & the [doc_guidelines template used](https://github.com/AustralianBioCommons/doc_guidelines) are supported by the Australian BioCommons via Bioplatforms Australia funding, the Australian Research Data Commons (https://doi.org/10.47486/PL105) and the Queensland Government RICF programme. Bioplatforms Australia and the Australian Research Data Commons are enabled by the National Collaborative Research Infrastructure Strategy (NCRIS).
input: HiFi reads as FASTQ
input: HiFiASM 1o assembly
is based on: https://workflowhub.eu/workflows/237
is part of: https://workflowhub.eu/collections/5
is part of: https://workflowhub.eu/collections/6
keywords: Assembly, purge_dups, HiFi
license: GPL-3.0
output: Purge overlaps on input dataset(s): purge_dups bed file
output: Purge overlaps on input dataset(s): purge_dups log file
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output: _anonymous_output_3
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output: _anonymous_output_5
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producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 2

Andrea Zaliani
type: Person

Jupyter
type: ComputerLanguage

Calculate the half maximal inhibitory concentration (IC50) for each compound used in a SARS-CoV-2 study
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/144
creator: Andrea Zaliani
date created: 2021-11-16T10:19:16Z
date modified: 2023-01-16T13:55:00Z
description: # Summary This notebook demonstrates how to retrieve metadata associated to the paper [A SARS-CoV-2 cytopathicity dataset generated by high-content screening of a large drug repurposing collection](https://doi.org/10.1038/s41597-021-00848-4) and available in IDR at [idr0094-ellinger-sarscov2](https://idr.openmicroscopy.org/search/?query=Name:idr0094). Over 300 compounds were used in this investigation. This notebook allows the user to calculate the half maximal inhibitory concentration (IC50) for each compound. IC50 is a measure of the potency of a substance in inhibiting a specific biological or biochemical function. IC50 is a quantitative measure that indicates how much of a particular inhibitory substance (e.g. drug) is needed to inhibit, in vitro, a given biological process or biological component by 50%. User can download the IC50 for each compound used in that study The notebook can be launched in [My Binder](https://mybinder.org/v2/gh/IDR/idr0094-ellinger-sarscov2/master?urlpath=notebooks%2Fnotebooks%2Fidr0094-ic50.ipynb%3FscreenId%3D2603). A shiny app is also available for dynamic plotting of the IC50 curve for each compound. This R shiny app can be launched in [My Binder](https://mybinder.org/v2/gh/IDR/idr0094-ellinger-sarscov2/master?urlpath=shiny/apps/) # Inputs Parameters needed to configure the workflow: **screenId**: Identifier of a screen in IDR. # Ouputs Output file generated: **ic50.csv**: Comma separate value file containing the IC50 for each compound.
is part of: https://workflowhub.eu/collections/4
keywords: covid-19
license: BSD-2-Clause
producer: https://workflowhub.eu/projects/69
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 1

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alternate name: CWL

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exome-alignment
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type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/4
date created: 2021-11-19T10:06:37Z
date modified: 2023-01-16T13:55:02Z
description: Exome Alignment Workflow
input: fastq_files
input: readgroup
input: reference_fasta
input: sample_name
keywords: cancer, pediatric, Alignment
license: Apache-2.0
output: sorted_bam
producer: https://workflowhub.eu/projects/35
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Genetic Design
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creator: https://workflowhub.eu/people/18
date created: 2020-05-29T10:05:20Z
date modified: 2023-01-16T13:41:35Z
description: This workflow converts the top-ranking predicted pathways from the "RetroSynthesis" and "Pathway Analysis" workflows to plasmids intended to be expressed in the specified organism
input: GEM SBML
input: List of genetic parts
input: Max (UNIPROT) enzymes per reactions
input: Top pathways to convert to SBOL
input: rpSBML TAR
keywords: Retrosynthesis, genetic design, pathway prediction
license: MIT
output: Pathway SBOLs
output: _anonymous_output_10
output: _anonymous_output_6
output: _anonymous_output_7
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sd publisher: about.workflowhub.eu
version: 1

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exome-samtools
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type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/4
date created: 2021-11-19T10:11:36Z
date modified: 2023-01-16T13:55:08Z
description: Exome SAMtools Workflow
keywords: cancer, pediatric, SAMTools
license: Apache-2.0
producer: https://workflowhub.eu/projects/35
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

atavide
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-11-21T05:26:02Z
date modified: 2023-01-16T13:55:08Z
description: `atavide` is a complete workflow for metagenomics data analysis, including QC/QA, optional host removal, assembly and cross-assembly, and individual read based annotations. We have also built in some advanced analytics including tools to assign annotations from reads to contigs, and to generate metagenome-assembled genomes in several different ways, giving you the power to explore your data! `atavide` is 100% snakemake and conda, so you only need to install the snakemake workflow, and then everything else will be installed with conda. Steps: 1. QC/QA with [prinseq++](https://github.com/Adrian-Cantu/PRINSEQ-plus-plus) 2. optional host removal using bowtie2 and samtools, [as described previously](https://edwards.flinders.edu.au/command-line-deconseq/). To enable this, you need to provide a path to the host db and a host db. Metagenome assembly 1. pairwise assembly of each sample using [megahit](https://github.com/voutcn/megahit) 2. extraction of all reads that do not assemble using samtools flags 3. assembly of all unassembled reads using [megahit](https://github.com/voutcn/megahit) 4. compilation of _all_ contigs into a single unified set using [Flye](https://github.com/fenderglass/Flye) 5. comparison of reads -> contigs to generate coverage MAG creation 1. [metabat](https://bitbucket.org/berkeleylab/metabat/src/master/) 2. [concoct](https://github.com/BinPro/CONCOCT) 3. Pairwise comparisons using [turbocor](https://github.com/dcjones/turbocor) followed by clustering Read-based annotations 1. [Kraken2](https://ccb.jhu.edu/software/kraken2/) 2. [singlem](https://github.com/wwood/singlem) 3. [SUPER-focus](https://github.com/metageni/SUPER-FOCUS) 4. [FOCUS](https://github.com/metageni/FOCUS) Want something else added to the suite? File an issue on github and we'll add it ASAP! ### Installation You will need to install 1. The NCBI taxonomy database somewhere 2. The superfocus databases somewhere, and set the SUPERFOCUS_DB environmental variable Everything else should ins…
license: MIT
producer: FAME
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Jupyter
type: ComputerLanguage

Diabetes related genes expressed in pancreas
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/144
date created: 2021-11-23T11:28:07Z
date modified: 2026-02-19T16:17:04Z
description: # Summary This notebook shows how to integrate genomic and image data resources. This notebook looks at the question **Which diabetes related genes are expressed in the pancreas?** Steps: * Query humanmine.org, an integrated database of Homo sapiens genomic data using the intermine API to find the genes. * Using the list of found genes, search in the Image Data Resource (IDR) for images linked to the genes, tissue and disease. * We use the [intermine Python API](https://github.com/intermine/intermine-ws-python) and the IDR Python API. The notebook can be opened in [Colab](https://colab.research.google.com/github/IDR/idr-notebooks/blob/master/humanmine.ipynb) # Inputs Parameters needed to configure the workflow: * TISSUE = "Pancreas" * DISEASE = "diabetes" # Ouputs * List of genes found using [HumanMine](https://pubmed.ncbi.nlm.nih.gov/35820040/) * List of images from IDR for one of the gene found
is part of: IDR searcher
is part of: https://workflowhub.eu/collections/4
license: BSD-2-Clause
producer: https://workflowhub.eu/projects/69
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 1

Javier Garrayo-Ventas
type: Person

Nextflow
type: ComputerLanguage

OpenEBench TCGA Cancer Driver Genes benchmarking workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/110
creator: Asier Gonzalez-Uriarte
creator: Javier Garrayo-Ventas
date created: 2021-11-29T15:21:23Z
date modified: 2023-01-16T13:55:10Z
description: ## Description The workflow takes an input file with Cancer Driver Genes predictions (i.e. the results provided by a participant), computes a set of metrics, and compares them against the data currently stored in OpenEBench within the TCGA community. Two assessment metrics are provided for that predictions. Also, some plots (which are optional) that allow to visualize the performance of the tool are generated. The workflow consists in three standard steps, defined by OpenEBench. The tools needed to run these steps are containerised in three Docker images, whose recipes are available in the [TCGA_benchmarking_dockers](https://github.com/inab/TCGA_benchmarking_dockers ) repository and the images are stored in the [INB GitLab container registry](https://gitlab.bsc.es/inb/elixir/openebench/workflows/tcga_benchmarking_dockers/container_registry) . Separated instances are spawned from these images for each step: 1. **Validation**: the input file format is checked and, if required, the content of the file is validated (e.g check whether the submitted gene IDs exist) 2. **Metrics Generation**: the predictions are compared with the 'Gold Standards' provided by the community, which results in two performance metrics - precision (Positive Predictive Value) and recall(True Positive Rate). 3. **Consolidation**: the benchmark itself is performed by merging the tool metrics with the rest of TCGA data. The results are provided in JSON format and SVG format (scatter plot). ![OpenEBench benchmarking workflow](https://raw.githubusercontent.com/inab/TCGA_benchmarking_workflow/1.0.8/workflow_schema.jpg) ## Data * [TCGA_sample_data](./TCGA_sample_data) folder contains all the reference data required by the steps. It is derived from the manuscript: [Comprehensive Characterization of Cancer Driver Genes and Mutations](https://www.cell.com/cell/fulltext/S0092-8674%2818%2930237-X?code=cell-site), Bailey et al, 2018, Cell [![doi:10.1016/j.cell.2018.02.060](https://img.shields.io/badge/doi-10.…
is based on: https://workflowhub.eu/workflows/244
keywords: tcga, openebench, benchmarking
license: LGPL-3.0
producer: OpenEBench
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 4

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Galaxy
type: ComputerLanguage

De novo digitisation
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2021-11-26T14:46:05Z
date modified: 2023-01-16T13:55:15Z
input: Catalog number
input: Higher classification
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input: Object type
input: Person identifier
input: Person name
input: Rights holder
keywords: Segmentation
license: Apache-2.0
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producer: https://workflowhub.eu/projects/72
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Simon Bray
type: Person

Tim Dudgeon
type: Person

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fragment-based-docking-scoring/main
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creator: Tim Dudgeon
creator: https://orcid.org/0000-0002-0621-6705
creator: Simon Bray
date created: 2023-02-17T03:01:49Z
date modified: 2025-12-12T02:01:35Z
description: Virtual screening of the SARS-CoV-2 main protease with rDock and pose scoring
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input: #main/Receptor (PDB)
input: #main/SuCOS threshold
is based on: https://workflowhub.eu/workflows/246
license: MIT
output: #main/Docking poses
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producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 4

Simon Bray
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Galaxy
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protein-ligand-complex-parameterization/main
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Simon Bray
date created: 2021-12-21T03:01:09Z
date modified: 2025-12-12T02:01:32Z
description: # Protein-ligand complex parameterization Parameterizes an input protein (PDB) and ligand (SDF) file prior to molecular dynamics simulation with GROMACS. This is a simple workflow intended for use as a subworkflow in more complex MD workflows. It is used as a subworkflow by the GROMACS MMGBSA and dcTMD workflows.
input: #main/Apoprotein PDB
input: #main/Force field
input: #main/Ligand SDF
input: #main/Water model
input: #main/pH
is based on: https://workflowhub.eu/workflows/247
license: MIT
output: #main/Complex GRO
output: #main/Complex topology
output: #main/Position restraints file
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 2

Simon Bray
type: Person

Galaxy
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gromacs-mmgbsa/main
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creator: https://orcid.org/0000-0002-0621-6705
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date created: 2026-03-10T03:02:02Z
date modified: 2026-03-10T03:02:02Z
description: MMGBSA simulation and calculation
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is based on: https://workflowhub.eu/workflows/248
license: MIT
output: #main/Complex GRO
output: #main/Complex topology
output: #main/GRO files
output: #main/GROMACS setup (ITP) on input dataset(s)
output: #main/MMGBSA free energy
output: #main/MMGBSA statistics
output: #main/XTC files
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 7

Simon Bray
type: Person

Galaxy
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gromacs-dctmd/main
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0002-0621-6705
creator: Simon Bray
date created: 2026-02-20T03:02:00Z
date modified: 2026-02-20T03:02:00Z
description: Perform dcTMD free energy simulations and calculations
input: Force field
input: Ligand SDF
input: Number of equilibration steps
input: Number of simulations
input: Number of steps
input: Protein PDB
input: Protein pull group
input: Pull group pbcatom
input: Pulling rate
input: Salt concentration
input: Step length (ps)
input: Temperature
input: Water model
input: pH to protonate ligand
is based on: https://workflowhub.eu/workflows/249
license: MIT
output: Free energy data
output: Friction data
output: GRO files
output: GROMACS setup (ITP) on input dataset(s)
output: Log
output: Structure file (GRO format, optional)
output: TPR files
output: Topology
output: XTC files
output: XVG files
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 5

RetroSynthesis
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type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/18
date created: 2020-05-29T10:08:17Z
date modified: 2023-01-16T13:41:42Z
description: Generate possible metabolic routes for the production of a target molecule in an organism of choice
input: GEM SBML
input: Maximal Pathway Length
input: Target InChI
keywords: Retrosynthesis, pathway prediction, pathway design, Synthetic Biology, metabolic engineering
license: MIT
output: SBML Pathways
output: _anonymous_output_10
output: _anonymous_output_3
output: _anonymous_output_5
output: _anonymous_output_6
output: _anonymous_output_7
output: _anonymous_output_8
output: _anonymous_output_9
producer: https://workflowhub.eu/projects/1
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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Mar Batlle
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alternate name: CWL

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counts
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metadata
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multiAffinity
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creator: https://workflowhub.eu/people/4
creator: Mar Batlle
date created: 2021-12-14T10:01:12Z
date modified: 2023-04-21T11:42:40Z
description: MultiAffinity enables the study of how gene dysregulation propagates on a multilayer network on a disease of interest, uncovering key genes. Find the detailed documentation for the tool [here](https://marbatlle.github.io/multiAffinity/). ![alt](https://github.com/marbatlle/multiAffinity/raw/main/docs/img/multiAffinity_workflow.png)
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input: Molti_Louvain
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input: control_id
input: counts
input: layers
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input: multiXrank_selfloops
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keywords: cancer, pediatric, rna-seq, networks, community-detection
license: Apache-2.0
output: output
producer: https://workflowhub.eu/projects/35
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

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Investigation of lockdown effect on air quality between January 2019 to May 2021.
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date created: 2021-12-20T09:43:13Z
date modified: 2023-01-16T13:56:22Z
description: This workflow extracts 5 different time periods e.g. January- June 2019, 2020 and 2021, July-December 2019 and 2020 over a single selected location. Then statistics (mean, minimum, maximum) are computed. The final products are maximum, minimum and mean.
keywords: RELIANCE, copernicus, air-quality
license: Apache-2.0
producer: https://workflowhub.eu/projects/18
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

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CAMS-PM2_5-20211222_netcdf
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Pangeo 101 for everyone - introduction to Xarray
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type: SoftwareSourceCode
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date created: 2021-12-29T07:57:46Z
date modified: 2023-01-16T13:56:30Z
description: Abstract CWL Automatically generated from the Galaxy workflow file: GTN 'Pangeo 101 for everyone - Introduction to Xarray'. In this tutorial, we analyze particle matter < 2.5 μm/m3 data from Copernicus Atmosphere Monitoring Service to understand Xarray Galaxy Tools: - Understand how an Xarray dataset is organized; - Get metadata from Xarray dataset such as variable names, units, coordinates (latitude, longitude, level), etc; - Plot an Xarray dataset on a geographical map and learn to customize it; - Select/Subset an Xarray dataset from coordinates values such as time selection or a subset over a geographical area; - Mask an Xarray dataset with a Where statement, for instance to only see PM2.5 > 30 μm/m and highlight on a map regions with "high" values; - Convert an Xarray dataset to Tabular data (pandas dataframe); - Plot tabular data to visualize the forecast PM2.5 over a single point (here Naples) using a scatterplot and/or climate stripes.
input: 0_Input Parameter
input: 10_Input Parameter
input: 2_Input Parameter
input: 3_Input Parameter
input: 4_Input Parameter
input: 5_Input Parameter
input: 6_Input Parameter
input: 7_Input Parameter
input: 8_Input Parameter
input: 9_Input Parameter
input: CAMS-PM2_5-20211222_netcdf
keywords: GTN, Climate, copernicus, pangeo
license: MIT
producer: https://workflowhub.eu/projects/18
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
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Nanopore Guppy Basecalling Assembly Workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/227
creator: https://workflowhub.eu/people/46
creator: https://workflowhub.eu/people/84
date created: 2022-01-06T07:36:38Z
date modified: 2023-01-16T13:56:37Z
description: ### - deprecated - Workflow for sequencing with ONT Nanopore, from basecalling to assembly. - Guppy (basecalling of raw reads) - MinIONQC (quality check) - FASTQ merging from multi into one file - Kraken2 (taxonomic classification) - Krona (classification visualization) - Flye (de novo assembly) - Medaka (assembly polishing) - QUAST (assembly quality reports) **All tool CWL files and other workflows can be found here:** Tools: https://git.wur.nl/unlock/cwl/-/tree/master/cwl Workflows: https://git.wur.nl/unlock/cwl/-/tree/master/cwl/workflows
input: basecalling model
input: configuration_command
input: nanopore reads
input: identifier used
input: kraken_database
input: number of threads
license: Apache-2.0
output: Flye de novo assembler for single-molecule reads
output: Guppy for CPU
output: Kraken2 reports
output: Krona taxonomy visualization
output: Medaka polisher
output: FASTQ files merged
output: MinION-Quality-Check
output: QUAlity assessment
producer: https://workflowhub.eu/projects/16
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

Basecalling model
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Contamination reference file(s)
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illumina forward reads
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Kraken2 database
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When working with metagenomes
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Nanopore Assembly Workflow - Deprecated -
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type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/227
creator: https://workflowhub.eu/people/46
creator: https://workflowhub.eu/people/84
date created: 2022-04-20T09:12:09Z
date modified: 2023-02-02T15:16:21Z
description: #### - Deprecated - #### See our updated hybrid assembly workflow: https://workflowhub.eu/workflows/367 #### And other workflows: https://workflowhub.eu/projects/16#workflows # **Workflow for sequencing with ONT Nanopore data, from basecalled reads to (meta)assembly and binning** - Workflow Nanopore Quality - Kraken2 taxonomic classification of FASTQ reads - Flye (de-novo assembly) - Medaka (assembly polishing) - metaQUAST (assembly quality reports) **When Illumina reads are provided:** - Workflow Illumina Quality: https://workflowhub.eu/workflows/336?version=1 - Assembly polishing with Pilon - Workflow binnning https://workflowhub.eu/workflows/64?version=11 - Metabat2 - CheckM - BUSCO - GTDB-Tk **All tool CWL files and other workflows can be found here:** Tools: https://git.wur.nl/unlock/cwl/-/tree/master/cwl Workflows: https://git.wur.nl/unlock/cwl/-/tree/master/cwl/workflows
input: Basecalling model
input: Run binning workflow
input: Deduplicate reads
input: Contamination reference file(s)
input: Identifier used
input: illumina forward reads
input: illumina reverse reads
input: Kraken2 database
input: Maximum memory in MB
input: When working with metagenomes
input: Nanopore reads
input: Pilon fix list
input: Number of threads
input: Use mapped reads
is based on: https://workflowhub.eu/workflows/254
keywords: nanopore, Genomics, Metagenomics
license: Apache-2.0
output: Assembly output
output: Binning output
output: Filtered statistics
output: Kraken2 reports
output: Read quality and filtering reports
producer: https://workflowhub.eu/projects/16
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 3

Common Workflow Language
type: ComputerLanguage
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Config file
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CWL GMX Automatic Ligand Parameterization tutorial
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creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T12:33:33Z
date modified: 2026-03-24T12:36:28Z
description: # Automatic Ligand parameterization tutorial using BioExcel Building Blocks (biobb) *** This tutorial aims to illustrate the process of **ligand parameterization** for a **small molecule**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Sulfasalazine** protein (3-letter code SAS), used to treat rheumatoid arthritis, ulcerative colitis, and Crohn's disease. **OpenBabel and ACPype** packages are used to **add hydrogens, energetically minimize the structure**, and **generate parameters** for the **GROMACS** package. With *Generalized Amber Force Field (GAFF) forcefield and AM1-BCC* charges. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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is based on: https://workflowhub.eu/workflows/255
license: Apache-2.0
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producer: https://workflowhub.eu/projects/11
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 3

Common Workflow Language
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Protein-ligand docking (fpocket)
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creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2022-01-10T11:48:32Z
date modified: 2023-01-16T13:56:53Z
description: This workflow performs the process of protein-ligand docking, step by step, using the BioExcel Building Blocks library (biobb).
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input: step1_fpocket_select_input_pockets_zip
input: step1_fpocket_select_output_pocket_pdb
input: step1_fpocket_select_output_pocket_pqr
input: step2_box_config
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input: step6_babel_convert_pose_pdb_output_path
license: Apache-2.0
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producer: https://workflowhub.eu/projects/11
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version: 1

Common Workflow Language
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CWL Protein Ligand Complex MD Setup tutorial
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creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T14:21:31Z
date modified: 2026-03-24T14:30:19Z
description: # Protein Ligand Complex MD Setup tutorial using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/complex/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation system** containing a **protein in complex with a ligand**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **T4 lysozyme** L99A/M102Q protein (PDB code 3HTB), in complex with the **2-propylphenol** small molecule (3-letter Code JZ4). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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CWL Protein-ligand Docking tutorial (Fpocket)
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creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T15:24:38Z
date modified: 2026-03-24T15:28:35Z
description: # Protein-ligand Docking tutorials using BioExcel Building Blocks (biobb) This tutorials aim to illustrate the process of **protein-ligand docking**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular examples used are based on the **Mitogen-activated protein kinase 14** (p38-α) protein (PDB code [3HEC](https://www.rcsb.org/structure/3HEC)), a well-known **Protein Kinase enzyme**, in complex with the FDA-approved **Imatinib** (PDB Ligand code [STI](https://www.rcsb.org/ligand/STI), DrugBank Ligand Code [DB00619](https://go.drugbank.com/drugs/DB00619)) and **Dasatinib** (PDB Ligand code [1N1](https://www.rcsb.org/ligand/1N1), DrugBank Ligand Code [DB01254](https://go.drugbank.com/drugs/DB01254)), small **kinase inhibitors** molecules used to treat certain types of **cancer**. The tutorials will guide you through the process of identifying the **active site cavity** (pocket) without previous knowledge, and the final prediction of the **protein-ligand complex**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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Pathway Ranker
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date created: 2020-05-29T11:19:05Z
date modified: 2023-01-16T13:41:47Z
description: The workflow runs the RetroSynthesis algorithm to generate a collection of heterologous pathways in a host organism of choice, converts them to SBML files, performs analysis on the pathways to then rank the theoretical best performing ones.
input: GEM SBML
input: Maximal Pathway Length
input: Target InChI
input: Top Ranking Pathways
keywords: pathway prediction, pathway design, metabolic engineering, Synthetic Biology, Retrosynthesis
license: MIT
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Alexandre Almeida
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Guillermo Rangel-Pineros and Ekaterina Sakharova
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Martin Hölzer
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date created: 2026-03-26T10:02:33Z
date modified: 2026-03-26T10:13:47Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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date created: 2026-03-26T11:16:30Z
date modified: 2026-03-26T11:22:37Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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CWL ABC MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T11:49:12Z
date modified: 2026-03-26T11:55:27Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
input: Config file
input: Input file
input: Output file

Snakemake
type: ComputerLanguage

polya_liftover
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Ryan Patterson-Cross
date created: 2022-01-17T10:44:28Z
date modified: 2023-01-16T13:57:28Z
description: # polya_liftover - sc/snRNAseq Snakemake Workflow A [Snakemake][sm] workflow for using PolyA_DB and UCSC Liftover with Cellranger. Some genes are not accurately annotated in the reference genome. Here, we use information provide by the [PolyA_DB v3.2][polya] to update the coordinates, then the [USCS Liftover][liftover] tool to update to a more recent genome. Next, we use [Cellranger][cr] to create the reference and count matrix. Finally, by taking advantage of the integrated [Conda][conda] and [Singularity][sing] support, we can run the whole thing in an isolated environment. Please see our [README][readme] for the full details! [sm]: https://snakemake.readthedocs.io/en/stable/index.html "Snakemake" [polya]: https://exon.apps.wistar.org/polya_db/v3/index.php "PolyA_DB" [liftover]: https://genome.ucsc.edu/cgi-bin/hgLiftOver "Liftover" [cr]: https://github.com/alexdobin/STAR "Cellranger" [conda]: https://docs.conda.io/en/latest/ "Conda" [sing]: https://sylabs.io/singularity/ "Singularity" [readme]: https://github.com/IMS-Bio2Core-Facility/polya_liftover/blob/main/README.md
keywords: Transcriptomics, scRNA-seq, Snakemake, FAIR workflows, FastQC, MultiQC, Cellranger, LiftOver
license: MIT
producer: Bioinformatics and Biostatistics (BIO2 ) Core
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

RNA sequencing data obtained from organisms with a reference genome and annotation followed by a prediction step of editing sites using RDDpred
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2022-01-27T10:44:25Z
date modified: 2023-01-16T13:57:29Z
description: ## Introduction **vibbits/rnaseq-editing** is a bioinformatics pipeline that can be used to analyse RNA sequencing data obtained from organisms with a reference genome and annotation followed by a prediction step of editing sites using RDDpred. The pipeline is largely based on the [nf-core RNAseq pipeline](https://nf-co.re/rnaseq/). The initial nf-core pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community! ## Pipeline summary 1. Merge re-sequenced FastQ files ([`cat`](http://www.linfo.org/cat.html)) 2. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)) 3. Adapter and quality trimming ([`Trimmomatics`](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)) 4. Use of STAR for multiple alignment and quantification: [`STAR`](https://github.com/alexdobin/STAR) 5. Sort and index alignments ([`SAMtools`](https://sourceforge.net/projects/samtools/files/samtools/)) 6. Prediction of editing sites using RDDpred ([`RDDpred`](https://github.com/vibbits/RDDpred)) 7. Extensive quality control: 1. [`RSeQC`](http://rseqc.sourceforge.net/) 2. [`Qualimap`](http://qualimap.bioinfo.cipf.es/) 3. [`dupRadar`](https://bioconductor.org/packages/release/bioc/html/dupRadar.html) 8. Present QC for raw read, alignment, gene biotype, sample similarity, and strand-specificity checks ([`MultiQC`](http://multiqc.info/), [`R`](https:/…
license: MIT
producer: VIB Bioinformatics Core
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

FAIR CRCC - send data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/31
date created: 2022-01-27T16:12:12Z
date modified: 2025-12-12T02:01:34Z
description: # Snakemake workflow: FAIR CRCC - send data [![Snakemake](https://img.shields.io/badge/snakemake-≥6.3.0-brightgreen.svg)](https://snakemake.github.io) [![GitHub actions status](https://github.com/crs4/fair-crcc-send-data/workflows/Tests/badge.svg?branch=main)](https://github.com/crs4/fair-crcc-send-data/actions?query=branch%3Amain+workflow%3ATests) A Snakemake workflow for securely sharing Crypt4GH-encrypted sensitive data from the [CRC Cohort](https://www.bbmri-eric.eu/scientific-collaboration/colorectal-cancer-cohort/) to a destination approved through a successful [access request](https://www.bbmri-eric.eu/services/access-policies/). The recommendation is to create a directory for the request that has been approved; it will be used as the working directory for the run. Copy there the recipient's crypt4gh key and prepare the run configuration. The configuration will specify the repository, the destination of the data, and the list of files/directories to transfer. ## What's the CRC Cohort? The CRC Cohort is a collection of clinical data and digital high-resolution digital pathology images pertaining to tumor cases. The collection has been assembled from a number of participating biobanks and other partners through the [ADOPT BBMRI-ERIC](https://www.bbmri-eric.eu/scientific-collaboration/adopt-bbmri-eric/) project. Researchers interested in using the data for science can file an application for access. If approved, the part of the dataset required for the planned and approved work can be copied to the requester's selected secure storage location (using this workflow). ## Usage ### Example mkdir request_1234 && cd request_1234 # Now write the configuration, specifying crypt4gh keys, destination and files to send. # Finally, execute workflow. snakemake --snakefile ../fair-crcc-send-data/workflow/Snakefile --profile ../profile/ --configfile config.yml --use-singularity --cores #### Run configuration example ``` recipient_key: ./recipient_key repository: path: "/mnt/rb…
license: GPL-3.0
producer: https://workflowhub.eu/projects/80
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

FAIR CRCC - image conversion
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/31
date created: 2022-01-31T11:03:37Z
date modified: 2025-03-14T02:02:34Z
description: # Snakemake workflow: FAIR CRCC - image conversion [![Snakemake](https://img.shields.io/badge/snakemake-≥6.3.0-brightgreen.svg)](https://snakemake.github.io) [![GitHub actions status](https://github.com/crs4/fair-crcc-img-convert/workflows/Tests/badge.svg?branch=main)](https://github.com/crs4/fair-crcc-img-convert/actions?query=branch%3Amain+workflow%3ATests) A Snakemake workflow for converting whole-slide images (WSI) from the [CRC Cohort](https://www.bbmri-eric.eu/scientific-collaboration/colorectal-cancer-cohort/) from vendor-specific image formats to open image formats (at the moment, OME-TIFF). The workflow also encrypts the new image files with [Crypt4GH](https://doi.org/10.1093/bioinformatics/btab087). ## What's the CRC Cohort? The CRC Cohort is a collection of clinical data and digital high-resolution digital pathology images pertaining to tumor cases. The collection has been assembled from a number of participating biobanks and other partners through the [ADOPT BBMRI-ERIC](https://www.bbmri-eric.eu/scientific-collaboration/adopt-bbmri-eric/) project. Researchers interested in using the data for science can [apply for access](https://www.bbmri-eric.eu/services/access-policies/). ## Usage The usage of this workflow is described in the [Snakemake Workflow Catalog](https://snakemake.github.io/snakemake-workflow-catalog/?usage=crs4%2Ffair-crcc-img-convert). If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this repository and its DOI (see above).
license: GPL-3.0
producer: https://workflowhub.eu/projects/80
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Jupyter
type: ComputerLanguage

Cell Lineage in the adult mouse corpus callosum
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/144
date created: 2022-02-08T11:36:16Z
date modified: 2023-01-16T13:57:29Z
description: # Summary This notebook demonstrates how to recreate lineages published in the paper [Live imaging of remyelination in the adult mouse corpus callosum](https://www.pnas.org/content/118/28/e2025795118) and available at [idr0113-bottes-opcclones](https://idr.openmicroscopy.org/search/?query=Name:idr0113). The lineage is created from the metadata associated to the specified image. To load the data from the Image Data Resource, we use: * the [Python API](https://docs.openmicroscopy.org/omero/latest/developers/Python.html) * the [JSON API](https://docs.openmicroscopy.org/omero/latest/developers/json-api.html) LPC-induced focal demyelination and in vivo imaging of genetically targeted OPCs and their progeny to describe the cellular dynamics of OPC-mediated remyelination in the CC. Longitudinal observation of OPCs and their progeny for up to two months reveals functional inter- and intraclonal heterogeneity and provides insights into the cell division capacity and the migration/differentiation dynamics of OPCs and their daughter cells in vivo. The majority of the clones remained quiescent or divided only few times. Some OPCs were highly proliferative. Large clones showed longer times between consecutive divisions compared to low proliferating clones. OPCs show distinct modes of cell division: from symmetric proliferative, to symmetric differentiating and also asymmetric cell division, where the OPC is self-renewed while the other daughter cell differentiates. Only 16.46% of OPC-derived cells differentiated into mature, remyelinating oligodendrocytes, with OPCs born at early divisions showing a higher probability to survive and to terminally differentiate. Cell death was associated with distinct cell division histories of different clones, with higher probability of death when generated at later divisions. Migratory behaviour was restricted to progenitors. Successfully differentiating progenitors moved shorter distances per day compared to dying cells. # Inputs Parameters n…
is part of: https://workflowhub.eu/collections/4
license: BSD-2-Clause
producer: https://workflowhub.eu/projects/69
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VIRify
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/27
creator: Alexandre Almeida
creator: Guillermo Rangel-Pineros and Ekaterina Sakharova
creator: Martin Hölzer
date created: 2020-06-08T10:21:08Z
date modified: 2023-01-16T13:41:53Z
description: VIRify VIRify is a recently developed pipeline for the detection, annotation, and taxonomic classification of viral contigs in metagenomic and metatranscriptomic assemblies. The pipeline is part of the repertoire of analysis services offered by MGnify. VIRify’s taxonomic classification relies on the detection of taxon-specific profile hidden Markov models (HMMs), built upon a set of 22,014 orthologous protein domains and referred to as ViPhOGs. VIRify was implemented in CWL. What do I need? The current implementation uses CWL version 1.2 dev+2. It was tested using Toil version 4.10 as the workflow engine and conda to manage the software dependencies. Docker - Singularity support Soon… Setup environment conda env create -f cwl/requirements/conda_env.yml conda activate viral_pipeline Basic execution cd cwl/ virify.sh -h A note about metatranscriptomes Although VIRify has been benchmarked and validated with metagenomic data in mind, it is also possible to use this tool to detect RNA viruses in metatranscriptome assemblies (e.g. SARS-CoV-2). However, some additional considerations for this purpose are outlined below: 1. Quality control: As for metagenomic data, a thorough quality control of the FASTQ sequence reads to remove low-quality bases, adapters and host contamination (if appropriate) is required prior to assembly. This is especially important for metatranscriptomes as small errors can further decrease the quality and contiguity of the assembly obtained. We have used TrimGalore for this purpose. 2. Assembly: There are many assemblers available that are appropriate for either metagenomic or single-species transcriptomic data. However, to our knowledge, there is no assembler currently available specifically for metatranscriptomic data. From our preliminary investigations, we have found that transcriptome-specific assemblers (e.g. rnaSPAdes) generate more contiguous and complete metatranscriptome assemblies compared to metagenomic alternatives (e.g. MEGAHIT and meta…
input: add_hmms_tsv
input: hmmscan_database_dir
input: img_blast_database_dir
input: input_fasta_file
input: mashmap_reference_file
input: ncbi_tax_db_file
input: pprmeta_simg
input: virsorter_data_dir
input: virsorter_virome
keywords: covid-19
license: Apache-2.0
output: blast_merged_tsvs
output: blast_result_filtereds
output: blast_results
output: filtered_contigs
output: high_confidence_contigs
output: high_confidence_faa
output: krona_plot_all
output: krona_plots
output: low_confidence_contigs
output: low_confidence_faa
output: mashmap_hits
output: parse_prophages_contigs
output: prophages_faa
output: taxonomy_assignations
output: virfinder_output
output: virsorter_output_fastas
producer: https://workflowhub.eu/projects/9
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Alexandre Almeida
type: Person

Guillermo Rangel-Pineros and Ekaterina Sakharova
type: Person

Martin Hölzer
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Nextflow
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Galaxy
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Galaxy workflow demonstrating the usage of EODIE Galaxy Tool
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creator: https://workflowhub.eu/people/49
date created: 2022-03-11T12:32:55Z
date modified: 2023-01-16T13:57:33Z
description: This workflow demonstrates the usage of EODIE, a toolkit to extract object based timeseries information from Earth Observation data. EODIE is a toolkit to extract object based timeseries information from Earth Observation data. The EODIE code can be found on [Gitlab](https://gitlab.com/fgi_nls/public/EODIE) . The goal of EODIE is to ease the extraction of time series information at object level. Today, vast amounts of Earth Observation data are available to the users via for example earth explorer or scihub. Often, not the whole images are needed for exploitation, but only the timeseries of a certain feature on object level. Objects may be polygons depicting agricultural field parcels, forest plots, or areas of a certain land cover type. EODIE takes the objects in as polygons in a shapefile as well as the timeframe of interest and the features (eg vegetation indices) to be extracted. The output is a per polygon timeseries of the selected features over the timeframe of interest. **Online documentation** EODIE documentation can be found [here](https://eodie.readthedocs.io/en/latest/). **Abstract CWL** Automatically generated from the Galaxy workflow file: Workflow constructed from history 'EODIE Sentinel'
input: sentinel2_tiles_world
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keywords: earth observation, copernicus, ndvi, sentinel-2 data
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programming language: Galaxy
sd publisher: about.workflowhub.eu
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Python
type: ComputerLanguage

Python Protein MD Setup tutorial
type: ComputationalWorkflow
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creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T10:09:38Z
date modified: 2026-03-24T10:12:24Z
description: # Protein MD Setup tutorial using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Lysozyme** protein (PDB code 1AKI). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/276
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programming language: Python
sd publisher: about.workflowhub.eu
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Galaxy
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Galaxy Protein MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2023-05-03T13:41:05Z
date modified: 2023-05-03T13:43:16Z
description: # Protein MD Setup tutorial using BioExcel Building Blocks (biobb) *** ## This workflow must be run in **biobb.usegalaxy.es**. Please, [click here to access](https://biobb.usegalaxy.es/u/gbayarri/w/gmx-protein-md-setup). *** **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Lysozyme** protein (PDB code 1AKI). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2023 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2023 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/277
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producer: https://workflowhub.eu/projects/11
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 3

Daniel López-López
type: Person

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bits_set
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blacklist
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codex_max_len
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samples
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threads_samtools
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fastqc_raw_zip
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output_manta
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Common Workflow Language
type: ComputerLanguage
alternate name: CWL

CNV_pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/4
creator: Daniel López-López
date created: 2022-10-11T11:04:44Z
date modified: 2023-01-16T13:57:59Z
description: # StructuralVariants Workflow
input: bed
input: bits_set
input: blacklist
input: bwt_algorithm
input: codex_max_len
input: codex_min_len
input: codex_min_lratio
input: enable_codex
input: enable_exomeDepth
input: enable_gridss
input: enable_manta
input: exomeDepth_max_len
input: exomeDepth_min_bf
input: exomeDepth_min_len
input: fastq1
input: fastq2
input: generate_bwa_indexes
input: gridss_max_len
input: gridss_min_len
input: gridss_min_q
input: manta_exome
input: manta_max_len
input: manta_min_len
input: manta_min_q
input: min_mapping_quality
input: read_group
input: reference_amb
input: reference_ann
input: reference_bwt
input: reference_fai
input: reference_fasta
input: reference_pac
input: reference_sa
input: samples
input: threads_bwa_mem
input: threads_fastp
input: threads_fastqc
input: threads_gridss
input: threads_samtools
is based on: https://workflowhub.eu/workflows/278
keywords: cancer, CODEX2, ExomeDepth, manta, TransBioNet, variant calling, GRIDSS, structural variants
license: Apache-2.0
output: fastqc_paired_html
output: fastqc_paired_zip
output: fastqc_raw_html
output: fastqc_raw_zip
output: html_report
output: json_report
output: output_all
output: output_bam_filtering
output: output_codex
output: output_exomedepth
output: output_gridss
output: output_manta
producer: https://workflowhub.eu/projects/70
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 11

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

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CWL Protein MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T09:34:44Z
date modified: 2026-03-24T09:51:08Z
description: # Protein MD Setup tutorial using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Lysozyme** protein (PDB code 1AKI). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
input: Input file
input: Output file
input: Config file
is based on: https://workflowhub.eu/workflows/279
license: Apache-2.0
output: output_structure_path
output: output_path
output: output_ndx_path
output: output_trr_path
output: output_gro_path
output: output_edr_path

VIRify
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/27
creator: Alexandre Almeida
creator: Guillermo Rangel-Pineros and Ekaterina Sakharova
creator: Martin Hölzer
date created: 2020-06-08T10:29:47Z
date modified: 2023-03-24T16:47:02Z
description: VIRify VIRify is a recently developed pipeline for the detection, annotation, and taxonomic classification of viral contigs in metagenomic and metatranscriptomic assemblies. The pipeline is part of the repertoire of analysis services offered by MGnify. VIRify’s taxonomic classification relies on the detection of taxon-specific profile hidden Markov models (HMMs), built upon a set of 22,014 orthologous protein domains and referred to as ViPhOGs. VIRify was implemented in CWL. What do I need? The current implementation uses CWL version 1.2 dev+2. It was tested using Toil version 4.10 as the workflow engine and conda to manage the software dependencies. Docker - Singularity support Soon… Setup environment conda env create -f cwl/requirements/conda_env.yml conda activate viral_pipeline Basic execution cd cwl/ virify.sh -h A note about metatranscriptomes Although VIRify has been benchmarked and validated with metagenomic data in mind, it is also possible to use this tool to detect RNA viruses in metatranscriptome assemblies (e.g. SARS-CoV-2). However, some additional considerations for this purpose are outlined below: 1. Quality control: As for metagenomic data, a thorough quality control of the FASTQ sequence reads to remove low-quality bases, adapters and host contamination (if appropriate) is required prior to assembly. This is especially important for metatranscriptomes as small errors can further decrease the quality and contiguity of the assembly obtained. We have used TrimGalore for this purpose. 2. Assembly: There are many assemblers available that are appropriate for either metagenomic or single-species transcriptomic data. However, to our knowledge, there is no assembler currently available specifically for metatranscriptomic data. From our preliminary investigations, we have found that transcriptome-specific assemblers (e.g. rnaSPAdes) generate more contiguous and complete metatranscriptome assemblies compared to metagenomic alternatives (e.g. MEGAHIT and meta…
keywords: covid-19
license: Apache-2.0
producer: https://workflowhub.eu/projects/153
producer: https://workflowhub.eu/projects/9
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

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alignments_are_sorted
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Python
type: ComputerLanguage

Python GMX Automatic Ligand Parameterization tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T12:36:41Z
date modified: 2026-03-24T12:38:51Z
description: # Automatic Ligand parameterization tutorial using BioExcel Building Blocks (biobb) *** This tutorial aims to illustrate the process of **ligand parameterization** for a **small molecule**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Sulfasalazine** protein (3-letter code SAS), used to treat rheumatoid arthritis, ulcerative colitis, and Crohn's disease. **OpenBabel and ACPype** packages are used to **add hydrogens, energetically minimize the structure**, and **generate parameters** for the **GROMACS** package. With *Generalized Amber Force Field (GAFF) forcefield and AM1-BCC* charges. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/280
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Python
sd publisher: about.workflowhub.eu
version: 6

Python
type: ComputerLanguage

Python Protein Ligand Complex MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T14:30:42Z
date modified: 2026-03-24T14:32:37Z
description: # Protein Ligand Complex MD Setup tutorial using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/complex/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation system** containing a **protein in complex with a ligand**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **T4 lysozyme** L99A/M102Q protein (PDB code 3HTB), in complex with the **2-propylphenol** small molecule (3-letter Code JZ4). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/281
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Python
sd publisher: about.workflowhub.eu
version: 5

Python
type: ComputerLanguage

Python Protein-ligand Docking tutorial (Fpocket)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T15:28:55Z
date modified: 2026-03-24T15:31:35Z
description: # Protein-ligand Docking tutorials using BioExcel Building Blocks (biobb) This tutorials aim to illustrate the process of **protein-ligand docking**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular examples used are based on the **Mitogen-activated protein kinase 14** (p38-α) protein (PDB code [3HEC](https://www.rcsb.org/structure/3HEC)), a well-known **Protein Kinase enzyme**, in complex with the FDA-approved **Imatinib** (PDB Ligand code [STI](https://www.rcsb.org/ligand/STI), DrugBank Ligand Code [DB00619](https://go.drugbank.com/drugs/DB00619)) and **Dasatinib** (PDB Ligand code [1N1](https://www.rcsb.org/ligand/1N1), DrugBank Ligand Code [DB01254](https://go.drugbank.com/drugs/DB01254)), small **kinase inhibitors** molecules used to treat certain types of **cancer**. The tutorials will guide you through the process of identifying the **active site cavity** (pocket) without previous knowledge, and the final prediction of the **protein-ligand complex**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/282
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Python
sd publisher: about.workflowhub.eu
version: 5

Python
type: ComputerLanguage

Python Amber Protein MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T10:14:05Z
date modified: 2026-03-26T10:16:07Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/283
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Python
sd publisher: about.workflowhub.eu
version: 5

Python
type: ComputerLanguage

Python Amber Protein Ligand Complex MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T11:23:14Z
date modified: 2026-03-26T11:25:26Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/284
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Python
sd publisher: about.workflowhub.eu
version: 5

Python
type: ComputerLanguage

Python ABC MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T11:55:41Z
date modified: 2026-03-26T11:57:47Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/285
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Python
sd publisher: about.workflowhub.eu
version: 5

Python
type: ComputerLanguage

Python Structural DNA helical parameters tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T14:02:53Z
date modified: 2026-03-26T14:04:55Z
description: # Structural DNA helical parameters from MD trajectory tutorial using BioExcel Building Blocks (biobb) **Based on the [NAFlex](https://mmb.irbbarcelona.org/NAFlex) server and in particular in its [Nucleic Acids Analysis section](https://mmb.irbbarcelona.org/NAFlex/help.php?id=tutorialAnalysisNA).** *** This tutorial aims to illustrate the process of **extracting structural and dynamical properties** from a **DNA MD trajectory helical parameters**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Drew Dickerson Dodecamer** sequence -CGCGAATTCGCG- (PDB code [1BNA](https://www.rcsb.org/structure/1BNA)). The trajectory used is a 500ns-long MD simulation taken from the [BigNASim](https://mmb.irbbarcelona.org/BIGNASim/) database ([NAFlex_DDD_II](https://mmb.irbbarcelona.org/BIGNASim/getStruc.php?idCode=NAFlex_DDD_II) entry). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/286
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Python
sd publisher: about.workflowhub.eu
version: 6

Python
type: ComputerLanguage

Python Protein MD Analysis tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T14:53:54Z
date modified: 2026-03-26T14:55:41Z
description: # Protein MD Analysis tutorial using BioExcel Building Blocks (biobb) *** This workflow computes a set of Quality Control (QC) analyses on top of an uploaded MD trajectory. QC analyses include positional divergence (RMSd), change of shape (Radius of Gyration), identification of flexible regions (atomic/residue fluctuations), and identification of different molecular conformations (trajectory clustering). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/287
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Python
sd publisher: about.workflowhub.eu
version: 4

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

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CWL Protein MD Setup tutorial with mutations
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-26T14:39:54Z
date modified: 2026-03-27T08:19:23Z
description: # Mutations Protein MD Setup tutorial using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Lysozyme** protein (PDB code 1AKI). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
input: List of mutations
input: Input file
input: Output file
input: Config file
is based on: https://workflowhub.eu/workflows/289
license: Apache-2.0
output: Collected Simulation Data
producer: https://workflowhub.eu/projects/11
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu

var-PE
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creator: Camille Juigné
date created: 2020-06-08T14:57:04Z
date modified: 2023-01-16T13:41:56Z
description: Analysis of variation within individual COVID-19 samples using bowtie2, bwa, fastp, multiqc , picard ,samtools, snpEff Workflow, tools and data are available on https://github.com/fjrmoreews/cwl-workflow-SARS-CoV-2/tree/master/Variation This worklow was ported into CWL from a Galaxy Workflow ( https://github.com/galaxyproject/SARS-CoV-2/tree/master/genomics/4-Variation migrated to CWL).
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description: # Mutations Protein MD Setup tutorial using BioExcel Building Blocks (biobb) **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Lysozyme** protein (PDB code 1AKI). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2026 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2026 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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Python GMX OPLS/AA Automatic Ligand Parameterization tutorial
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description: # Automatic Ligand parameterization tutorial using BioExcel Building Blocks (biobb) *** This tutorial aims to illustrate the process of **ligand parameterization** for a **small molecule**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Sulfasalazine** protein (3-letter code SAS), used to treat rheumatoid arthritis, ulcerative colitis, and Crohn's disease. **OpenBabel and ACPype** packages are used to **add hydrogens, energetically minimize the structure**, and **generate parameters** for the **GROMACS** package. With *Generalized Amber Force Field (GAFF) forcefield and AM1-BCC* charges. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2022 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2022 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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description: # Automatic Ligand parameterization tutorial using BioExcel Building Blocks (biobb) *** This tutorial aims to illustrate the process of **ligand parameterization** for a **small molecule**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Sulfasalazine** protein (3-letter code SAS), used to treat rheumatoid arthritis, ulcerative colitis, and Crohn's disease. **OpenBabel and ACPype** packages are used to **add hydrogens, energetically minimize the structure**, and **generate parameters** for the **GROMACS** package. With *Generalized Amber Force Field (GAFF) forcefield and AM1-BCC* charges. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2022 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2022 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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description: # Automatic Ligand parameterization tutorial using BioExcel Building Blocks (biobb) *** This tutorial aims to illustrate the process of **ligand parameterization** for a **small molecule**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Sulfasalazine** protein (3-letter code SAS), used to treat rheumatoid arthritis, ulcerative colitis, and Crohn's disease. **OpenBabel and ACPype** packages are used to **add hydrogens, energetically minimize the structure**, and **generate parameters** for the **GROMACS** package. With *Generalized Amber Force Field (GAFF) forcefield and AM1-BCC* charges. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2022 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2022 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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description: # Automatic Ligand parameterization tutorial using BioExcel Building Blocks (biobb) *** ## This workflow must be run in **biobb.usegalaxy.es**. Please, [click here to access](https://biobb.usegalaxy.es/u/gbayarri/w/gmx-ligand-parameterization). *** This tutorial aims to illustrate the process of **ligand parameterization** for a **small molecule**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **Sulfasalazine** protein (3-letter code SAS), used to treat rheumatoid arthritis, ulcerative colitis, and Crohn's disease. **OpenBabel and ACPype** packages are used to **add hydrogens, energetically minimize the structure**, and **generate parameters** for the **GROMACS** package. With *Generalized Amber Force Field (GAFF) forcefield and AM1-BCC* charges. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2023 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2023 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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Galaxy Protein Ligand Complex MD Setup
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description: # Protein Ligand Complex MD Setup tutorial using BioExcel Building Blocks (biobb) *** ## This workflow must be run in **biobb.usegalaxy.es**. Please, [click here to access](https://biobb.usegalaxy.es/u/gbayarri/w/gmx-protein-ligand-complex-md-setup). *** **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/complex/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation system** containing a **protein in complex with a ligand**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular example used is the **T4 lysozyme** L99A/M102Q protein (PDB code 3HTB), in complex with the **2-propylphenol** small molecule (3-letter Code JZ4). *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2022 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2022 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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Galaxy Protein-ligand Docking tutorial (Fpocket)
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description: # Protein-ligand Docking tutorials using BioExcel Building Blocks (biobb) *** ## This workflow must be run in **biobb.usegalaxy.es**. Please, [click here to access](https://biobb.usegalaxy.es/u/gbayarri/w/protein-ligand-docking). *** This tutorials aim to illustrate the process of **protein-ligand docking**, step by step, using the **BioExcel Building Blocks library (biobb)**. The particular examples used are based on the **Mitogen-activated protein kinase 14** (p38-α) protein (PDB code [3HEC](https://www.rcsb.org/structure/3HEC)), a well-known **Protein Kinase enzyme**, in complex with the FDA-approved **Imatinib** (PDB Ligand code [STI](https://www.rcsb.org/ligand/STI), DrugBank Ligand Code [DB00619](https://go.drugbank.com/drugs/DB00619)) and **Dasatinib** (PDB Ligand code [1N1](https://www.rcsb.org/ligand/1N1), DrugBank Ligand Code [DB01254](https://go.drugbank.com/drugs/DB01254)), small **kinase inhibitors** molecules used to treat certain types of **cancer**. The tutorials will guide you through the process of identifying the **active site cavity** (pocket) without previous knowledge, and the final prediction of the **protein-ligand complex**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2023 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2023 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
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Galaxy ABC MD Setup tutorial
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2023-05-03T13:54:07Z
date modified: 2023-05-03T13:55:38Z
description: # AMBER Protein MD Setup tutorials using BioExcel Building Blocks (biobb) *** ## This workflow must be run in **biobb.usegalaxy.es**. Please, [click here to access](https://biobb.usegalaxy.es/u/gbayarri/w/abcix-md-setup). *** **Based on the official [GROMACS tutorial](http://www.mdtutorials.com/gmx/lysozyme/index.html).** *** This tutorial aims to illustrate the process of **setting up a simulation** system containing a **protein**, step by step, using the **BioExcel Building Blocks library (biobb)** wrapping the **Ambertools MD package**. *** ## Copyright & Licensing This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)). * (c) 2015-2023 [Barcelona Supercomputing Center](https://www.bsc.es/) * (c) 2015-2023 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/) Licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details. ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")
is based on: https://workflowhub.eu/workflows/299
license: Apache-2.0
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Protein MD Setup tutorial using BioExcel Building Blocks (biobb) in CWL
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
date created: 2021-05-10T09:00:56Z
date modified: 2023-01-16T13:42:00Z
description: Common Workflow Language example that illustrate the process of setting up a simulation system containing a protein, step by step, using the [BioExcel Building Blocks](/projects/11) library (biobb). The particular example used is the Lysozyme protein (PDB code 1AKI). This workflow returns a resulting protein structure and simulated 3D trajectories.
input: step10_energy_min_config
input: step10_energy_min_name
input: step11_gppnvt_config
input: step13_energy_nvt_config
input: step13_energy_nvt_name
input: step14_gppnpt_config
input: step16_energy_npt_config
input: step16_energy_npt_name
input: step17_gppmd_config
input: step19_rmsfirst_config
input: step19_rmsfirst_name
input: step1_pdb_config
input: step1_pdb_name
input: step20_rmsexp_config
input: step20_rmsexp_name
input: step21_rgyr_config
input: step22_image_config
input: step23_dry_config
input: step4_editconf_config
input: step6_gppion_config
input: step7_genion_config
input: step8_gppmin_config
is based on: https://workflowhub.eu/workflows/29
is part of: https://workflowhub.eu/collections/1
is part of: https://workflowhub.eu/collections/3
keywords: molecular dynamics, trajectories, protein
license: Apache-2.0
output: Checkpoint file
output: Structures - Raw structure
output: Resulting protein structure
output: GROMACS topology file
output: Topologies GROMACS portable binary run
output: Trajectories - Raw trajectory
output: Trajectories - Post-processed trajectory
output: System Setup Observables - Potential Energy
output: System Setup Observables - Pressure and density
output: System Setup Observables - Temperature
output: Simulation Analysis
producer: https://workflowhub.eu/projects/11
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
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eQTL-Catalogue/qtlmap
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type: SoftwareSourceCode
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date created: 2022-03-29T16:10:54Z
date modified: 2023-01-16T13:59:09Z
description: # eQTL-Catalogue/qtlmap **Portable eQTL analysis and statistical fine mapping workflow used by the eQTL Catalogue** ### Introduction **eQTL-Catalogue/qtlmap** is a bioinformatics analysis pipeline used for QTL Analysis. The workflow takes phenotype count matrix (normalized and quality controlled) and genotype data as input, and finds associations between them with the help of sample metadata and phenotype metadata files (See [Input formats and preparation](docs/inputs_expl.md) for required input file details). To map QTLs, pipeline uses [QTLTools's](https://qtltools.github.io/qtltools/) PCA and RUN methods. For manipulation of files [BcfTools](https://samtools.github.io/bcftools/bcftools.html), [Tabix](http://www.htslib.org/doc/tabix.html) and custom [Rscript](https://www.rdocumentation.org/packages/utils/versions/3.5.3/topics/Rscript) scripts are used. The pipeline is built using [Nextflow](https://www.nextflow.io), a bioinformatics workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker / singularity containers making installation trivial and results highly reproducible. ### Documentation The eQTL-Catalogue/qtlmap pipeline comes with documentation about the pipeline, found in the `docs/` directory: 1. [Installation](docs/installation.md) 2. Pipeline configuration * [Local installation](docs/configuration/local.md) * [Adding your own system](docs/configuration/adding_your_own.md) 3. [Input formats and preparation](docs/inputs_expl.md) 4. [Running the pipeline](docs/usage.md) 5. [Troubleshooting](docs/troubleshooting.md) ### Pipeline Description Mapping QTLs is a process of finding statistically significant associations between phenotypes and genetic variants located nearby (within a specific window around phenotype, a.k.a cis window) This pipeline is designed to perform QTL mapping. It is intended to add this pipeline to the nf-core framework in the future. High level representation of the pipeline is shown…
license: MIT
producer: CINECA
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

V-pipe (main multi-virus version)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/22
creator: Kim Philipp Jablonski
date created: 2024-06-10T18:38:49Z
date modified: 2024-06-10T18:39:57Z
description: ![Logo](https://cbg-ethz.github.io/V-pipe/img/logo.svg) [![bio.tools](https://img.shields.io/badge/bio-tools-blue.svg)](https://bio.tools/V-Pipe) [![Snakemake](https://img.shields.io/badge/snakemake-≥7.11.0-blue.svg)](https://snakemake.github.io/snakemake-workflow-catalog/?usage=cbg-ethz/V-pipe) [![Deploy Docker image](https://github.com/cbg-ethz/V-pipe/actions/workflows/deploy-docker.yaml/badge.svg)](https://github.com/cbg-ethz/V-pipe/pkgs/container/v-pipe) [![Tests](https://github.com/cbg-ethz/V-pipe/actions/workflows/run_regression_tests.yaml/badge.svg)](https://github.com/cbg-ethz/V-pipe/actions/workflows/run_regression_tests.yaml) [![Mega-Linter](https://github.com/cbg-ethz/V-pipe/actions/workflows/mega-linter.yml/badge.svg)](https://github.com/cbg-ethz/V-pipe/actions/workflows/mega-linter.yml) [![License: Apache-2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) V-pipe is a workflow designed for the analysis of next generation sequencing (NGS) data from viral pathogens. It produces a number of results in a curated format (e.g., consensus sequences, SNV calls, local/global haplotypes). V-pipe is written using the Snakemake workflow management system. ## Usage Different ways of initializing V-pipe are presented below. We strongly encourage you to deploy it [using the quick install script](#using-quick-install-script), as this is our preferred method. To configure V-pipe refer to the documentation present in [config/README.md](config/README.md). V-pipe expects the input samples to be organized in a [two-level](config/README.md#samples) directory hierarchy, and the sequencing reads must be provided in a sub-folder named `raw_data`. Further details can be found on the [website](https://cbg-ethz.github.io/V-pipe/usage/). Check the utils subdirectory for [mass-importers tools](utils/README.md#samples-mass-importers) that can assist you in generating this hierarchy. We provide [virus-specific base configuration …
is based on: https://workflowhub.eu/workflows/301
keywords: Alignment, Assembly, covid-19, Genomics, INDELs, rna, SNPs, variant_calling, workflow
license: Apache-2.0
producer: https://workflowhub.eu/projects/7
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 5

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seurat scRNA-seq
type: ComputationalWorkflow
type: SoftwareSourceCode
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date created: 2022-04-14T13:28:34Z
date modified: 2023-01-16T13:59:13Z
input: barcodes
input: binning_method
input: block_size
input: clip_max
input: clusterDataR
input: featureSelectionDataR
input: features
input: filterDataR
input: findNeighborsR
input: find_markersR
input: k
input: loadDataR
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input: projectName
input: runPCAR
input: runTSNER
input: runUmapR
input: running_step
input: scaleDataR
input: scale_factor
input: selection_method
input: verbose
is based on: https://workflowhub.eu/workflows/303
license: Apache-2.0
output: clusterDataOutput
output: filterDataOutput
output: filterDataPlot
output: findFeaturesOutput
output: findFeaturesPlot
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output: loadDataOutput
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producer: Single Cell Unit
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 2

Galaxy
type: ComputerLanguage

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VGP genome profile analysis
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creator: https://workflowhub.eu/people/248
date created: 2022-04-05T11:22:34Z
date modified: 2023-01-16T13:59:21Z
description: Create Meryl Database used for the estimation of assembly parameters and quality control with Merqury. Part of the VGP pipeline.
input: Collection of Pacbio Data
keywords: vgp, Galaxy, Assembly
license: Apache-2.0
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Genomic variants - SNPs and INDELs detection using GATK4.
type: ComputationalWorkflow
type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/26
date created: 2020-06-17T06:11:59Z
date modified: 2023-01-16T13:42:19Z
description: Author: AMBARISH KUMAR er.ambarish@gmail.com & ambari73_sit@jnu.ac.in This is a proposed standard operating procedure for genomic variant detection using GATK4. It is hoped to be effective and useful for getting SARS-CoV-2 genome variants. It uses Illumina RNASEQ reads and genome sequence.
input: indices_folder
input: rnaseq_left_reads
input: rnaseq_right_reads
input: sars_cov_2_reference_genome
keywords: CWL, GATK4, SNPs, INDELs
license: Apache-2.0
output: indels
output: snps
producer: https://workflowhub.eu/projects/6
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

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VGP HiFi phased assembly with hifiasm and HiC data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/248
date created: 2022-04-05T21:55:13Z
date modified: 2023-01-16T13:59:26Z
description: Performs Long Read assembly using PacBio data and Hifiasm. Part of VGP assembly pipeline. This workflow generate a phased assembly.
input: HiC forward reads
input: HiC reverse reads
input: Meryl Database
input: Pacbio Reads Collection
keywords: vgp, Assembly, Galaxy
license: GPL-3.0
producer: https://workflowhub.eu/projects/24
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Genomic variants - SNPs and INDELs detection using VARSCAN2.
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/26
date created: 2020-06-17T06:24:44Z
date modified: 2023-01-16T13:42:27Z
description: Author: AMBARISH KUMAR er.ambarish@gmail.com; ambari73_sit@jnu.ac.in This is a proposed standard operating procedure for genomic variant detection using VARSCAN. It is hoped to be effective and useful for getting SARS-CoV-2 genome variants. It uses Illumina RNASEQ reads and genome sequence.
input: rnaseq_left_reads
input: rnaseq_right_reads
input: sample_name
input: sars_cov_2_reference_genome
keywords: CWL, SNPs, INDELs, VARSCAN2
license: Apache-2.0
output: indels
output: snps
producer: https://workflowhub.eu/projects/6
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

Genomescope model parameters
type: FormalParameter
conforms to: 1.0-RELEASE

Hifiasm Alternate assembly
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conforms to: 1.0-RELEASE

Hifiasm Primary assembly
type: FormalParameter
conforms to: 1.0-RELEASE

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conforms to: 1.0-RELEASE

VGP purge assembly with purge_dups pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/248
date created: 2022-04-05T22:16:05Z
date modified: 2023-01-16T13:59:31Z
description: Purge Phased assembly of duplications and overlaps. Include purge steps for Primary and Alternate assemblies.
input: Genomescope model parameters
input: Hifiasm Alternate assembly
input: Hifiasm Primary assembly
input: Pacbio Reads Collection - Trimmed
keywords: vgp, Assembly, Galaxy
license: GPL-3.0
producer: https://workflowhub.eu/projects/24
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

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conforms to: 1.0-RELEASE

Conflict resolution files
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Estimated genome size - Parameter File
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conforms to: 1.0-RELEASE

Hifiasm Purged Assembly
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conforms to: 1.0-RELEASE

Pacbio trimmed reads
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VGP hybrid scaffolding with Bionano optical maps
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/248
date created: 2022-04-05T22:21:34Z
date modified: 2023-01-16T13:59:34Z
description: Performs scaffolding using Bionano Data. Part of VGP assembly pipeline.
input: Bionano Data
input: Conflict resolution files
input: Estimated genome size - Parameter File
input: Hifiasm Purged Assembly
input: Pacbio trimmed reads
is part of: https://workflowhub.eu/collections/8
keywords: vgp, Assembly, Galaxy
license: GPL-3.0
producer: https://workflowhub.eu/projects/24
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

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type: FormalParameter
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VGP hybrid scaffolding with HiC data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/248
date created: 2022-04-05T23:33:36Z
date modified: 2023-01-16T13:59:36Z
description: Performs scaffolding using HiC Data. Part of VGP assembly pipeline. The scaffolding can be performed on long read assembly contigs or on scaffolds (e.g.: Bionano scaffolds).
input: Estimated genome size - Parameter File
input: HiC Forward reads
input: HiC reverse reads
input: Restriction enzyme sequences
input: Scaffolded Assembly
input: Sequence graph - Optional
is part of: https://workflowhub.eu/collections/8
keywords: vgp, Assembly, Galaxy
license: GPL-3.0
producer: https://workflowhub.eu/projects/24
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

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VGP HiFi phased assembly with hifiasm and HiC data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/248
date created: 2022-04-06T01:23:56Z
date modified: 2023-01-16T13:59:39Z
description: Performs Long Read assembly using PacBio data and Hifiasm. Part of VGP assembly pipeline. This workflow generate a phased assembly.
input: HiC forward reads
input: HiC reverse reads
input: Meryl Database
input: Pacbio Reads Collection
keywords: vgp, Assembly, Galaxy
license: GPL-3.0
producer: https://workflowhub.eu/projects/24
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Egon Willighagen
type: Person

Jupyter
type: ComputerLanguage

BridgeDb tutorial: Gene HGNC name to Ensembl identifier
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Marvin Martens
creator: Egon Willighagen
date created: 2022-04-06T13:12:39Z
date modified: 2023-01-16T13:59:41Z
description: # BridgeDb tutorial: Gene HGNC name to Ensembl identifier This tutorial explains how to use the BridgeDb identifier mapping service to translate HGNC names to Ensembl identifiers. This step is part of the OpenRiskNet use case to link Adverse Outcome Pathways to [WikiPathways](https://wikipathways.org/). First we need to load the Python library to allow calls to the [BridgeDb REST webservice](http://bridgedb.prod.openrisknet.org/swagger/): ```python import requests ``` Let's assume we're interested in the gene with HGNC MECP2 (FIXME: look up a gene in AOPWiki), the API call to make mappings is given below as `callUrl`. Here, the `H` indicates that the query (`MECP2`) is an HGNC symbol: ```python callUrl = 'http://bridgedb.prod.openrisknet.org/Human/xrefs/H/MECP2' ``` The default call returns all identifiers, not just for Ensembl: ```python response = requests.get(callUrl) response.text ``` 'GO:0001964\tGeneOntology\nuc065cav.1\tUCSC Genome Browser\n312750\tOMIM\nGO:0042551\tGeneOntology\nuc065car.1\tUCSC Genome Browser\nA0A087X1U4\tUniprot-TrEMBL\n4204\tWikiGenes\nGO:0043524\tGeneOntology\nILMN_1702715\tIllumina\n34355_at\tAffy\nGO:0007268\tGeneOntology\nMECP2\tHGNC\nuc065caz.1\tUCSC Genome Browser\nA_33_P3339036\tAgilent\nGO:0006576\tGeneOntology\nuc065cbg.1\tUCSC Genome Browser\nGO:0006342\tGeneOntology\n300496\tOMIM\nGO:0035176\tGeneOntology\nuc065cbc.1\tUCSC Genome Browser\nGO:0033555\tGeneOntology\nGO:0045892\tGeneOntology\nA_23_P114361\tAgilent\nGO:0045893\tGeneOntology\nENSG00000169057\tEnsembl\nGO:0090063\tGeneOntology\nGO:0005515\tGeneOntology\nGO:0002087\tGeneOntology\nGO:0005634\tGeneOntology\nGO:0007416\tGeneOntology\nGO:0008104\tGeneOntology\nGO:0042826\tGeneOntology\nGO:0007420\tGeneOntology\nGO:0035067\tGeneOntology\n300005\tOMIM\nNP_001104262\tRefSeq\nA0A087WVW7\tUniprot-TrEMBL\nNP_004983\tRefSeq\nGO:0046470\tGeneOntology\nGO:0010385\tGeneOntology\n11722682_at\tAffy\nGO:0051965\tGeneOntology\nNM_001316337\tRefSeq\nuc065caw.1\tUCSC Genome Browser\nA0A0…
is based on: https://workflowhub.eu/workflows/326
keywords: Toxicology, jupyter
license: GPL-3.0
producer: Toxicology community
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 3

Christina Adler
type: Person

Elena Martinez
type: Person

Fang Wang
type: Person

Henry Lydecker
type: Person

Smitha Sukumar
type: Person

Shell Script
type: ComputerLanguage

Shotgun-Metagenomics-Analysis
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/122
creator: https://workflowhub.eu/people/141
creator: https://workflowhub.eu/people/258
creator: Christina Adler
creator: Elena Martinez
creator: Fang Wang
creator: Henry Lydecker
creator: Smitha Sukumar
date created: 2022-04-07T00:45:10Z
date modified: 2023-01-16T13:59:41Z
description: # Shotgun Metagenomics Analysis Analysis of metagenomic shotgun sequences including assembly, speciation, ARG discovery and more ## Description The input for this analysis is paired end next generation sequencing data from metagenomic samples. The workflow is designed to be modular, so that individual modules can be run depending on the nature of the metagenomics project at hand. More modules will be added as we develop them - this repo is a work in progress! These scripts have been written specifically for NCI Gadi HPC, wich runs PBS Pro, however feel free to use and modify for anothre system if you are not a Gadi user. ### Part 1. Setup and QC Download the repo. You will see directories for `Fastq`, `Inputs`, `Reference` and `Logs`. You will need to copy or symlink your fastq to `Fastq`, sample configuration file (see below) to `Inputs` and the reference genome sequence of your host species (if applicable) to `Reference` for host contamination removal. #### Fastq inputs The scripts assume all fastq files are paired, gzipped, and all in the one directory named 'Fastq'. If your fastq are within a convoluted directory structure (eg per-sample directories) or you would simply like to link them from an alternate location, please use the script `setup_fastq.sh`. To use this script, parse the path name of your fastq as first argument on the command line, and run the script from the base working directory (/Shotgun-Metagenomics-Analysis) which will from here on be referred to as `workdir`. Note that this script looks for `f*q.gz` files (ie fastq.gz or fq.gz) - if yours differ in suffix, please adjust the script accordingly. ``` bash ./Scripts/setup_fastq.sh ``` #### Configuration/sample info The only required input configuration file should be named .config, where is the name of the current batch of samples you are processing, or some other meaningful name to your project; it will be used to name output files. The config file should be placed inside the $workdir/Inputs di…
is part of: https://workflowhub.eu/collections/6
keywords: Metagenomics, shotgun, antimicrobial resistance, humann2, bbmap, whole genome sequencing, Assembly, prokka, abricate, DIAMOND, kraken, braken
license: GPL-3.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Python
type: ComputerLanguage

Python Mutation Free Energy Calculations using BioExcel Building Blocks (biobb)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-24T15:09:19Z
date modified: 2026-03-24T15:10:58Z
description: # Mutation Free Energy Calculations using BioExcel Building Blocks (biobb) *** **Based on the official [pmx tutorial](http://pmx.mpibpc.mpg.de/sardinia2018_tutorial1/index.html).** *** This tutorial aims to illustrate how to compute a **fast-growth mutation free energy** calculation, step by step, using the BioExcel **Building Blocks library (biobb)**. The particular example used is the **Staphylococcal nuclease** protein (PDB code 1STN), a small, minimal protein, appropriate for a short tutorial. The **non-equilibrium free energy calculation** protocol performs a **fast alchemical transition** in the direction **WT->Mut** and back **Mut->WT**. The two equilibrium trajectories needed for the tutorial, one for **Wild Type (WT)** and another for the **Mutated (Mut)** protein (Isoleucine 10 to Alanine -I10A-), have already been generated and are included in this example. We will name **WT as stateA** and **Mut as stateB**. ![](https://raw.githubusercontent.com/bioexcel/biobb_wf_pmx_tutorial/master/biobb_wf_pmx_tutorial/notebooks/schema.png) The tutorial calculates the **free energy difference** in the folded state of a protein. Starting from **two 1ns-length independent equilibrium simulations** (WT and mutant), snapshots are selected to start **fast (50ps) transitions** driving the system in the **forward** (WT to mutant) and **reverse** (mutant to WT) directions, and the **work values** required to perform these transitions are collected. With these values, **Crooks Gaussian Intersection** (CGI), **Bennett Acceptance Ratio** (BAR) and **Jarzynski estimator** methods are used to calculate the **free energy difference** between the two states. *Please note that for the sake of disk space this tutorial is using 1ns-length equilibrium trajectories, whereas in the [original example](http://pmx.mpibpc.mpg.de/sardinia2018_tutorial1/eq.mdp) the equilibrium trajectories used were obtained from 10ns-length simulations.* *** ## Copyright & Licensing This software has been devel…
is based on: https://workflowhub.eu/workflows/328
license: Apache-2.0
producer: https://workflowhub.eu/projects/11
programming language: Python
sd publisher: about.workflowhub.eu
version: 6

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

rnaseq_left_reads
type: FormalParameter
conforms to: 1.0-RELEASE

rnaseq_right_reads
type: FormalParameter
conforms to: 1.0-RELEASE

sampleName
type: FormalParameter
conforms to: 1.0-RELEASE

sars_cov_2_reference_2bit_genome
type: FormalParameter
conforms to: 1.0-RELEASE

sars_cov_2_reference_genome
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conforms to: 1.0-RELEASE

indels
type: FormalParameter
conforms to: 1.0-RELEASE

snps
type: FormalParameter
conforms to: 1.0-RELEASE

Snakemake
type: ComputerLanguage

EJP-RD WP13 case-study: CAKUT peptidome and miRNome data analysis using the DIABLO and PLS-DA methods from the mixOmics R package
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/134
creator: https://workflowhub.eu/people/135
creator: https://workflowhub.eu/people/136
creator: https://workflowhub.eu/people/251
date created: 2022-04-20T16:43:51Z
date modified: 2023-01-16T13:59:42Z
description: For integrative analysis of CAKUT multi-omics data DIABLO method of the mixOmics package (version 6.10.9. Singh et. al. 2019) was used with sPLS-DA (sparse Partial Least Squares Discriminant Analysis Discriminant Analysis) and PLS-DA classification.
keywords: rare diseases
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/40
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

EJP-RD WP13 case-study: CAKUT proteome, peptidome and miRNome data analysis using WikiPathways
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/134
creator: https://workflowhub.eu/people/135
creator: https://workflowhub.eu/people/136
creator: Woosub Shin
creator: https://workflowhub.eu/people/251
date created: 2022-04-20T16:59:50Z
date modified: 2023-01-16T13:59:42Z
description: In this analysis, we created an extended pathway, using the WikiPathways repository (Version 20210110) and the three -omics datasets. For this, each of the three -omics datasets was first analyzed to identify differentially expressed elements, and pathways associated with the significant miRNA-protein links were detected. A miRNA-protein link is deemed significant, and may possibly be implying causality, if both a miRNA and its target are significantly differentially expressed. The peptidome and the proteome datasets were quantile normalized and log2 transformed (Pan and Zhang 2018; Zhao, Wong, and Goh 2020). Before transformation, peptide IDs were mapped to protein IDs, using the information provided by the data uploaders, and were summarized into single protein-level values using geometric mean. The miRNome dataset was already normalized and transformed; thus, the information of their targeting genes was simply added to each miRNA ID, using the information provided by miTaRBase (Huang et al. 2019). As a result, all three datasets had been mapped to their appropriate gene product-level (or, protein-level) identifiers.
keywords: rare diseases, Pathway Analysis, workflow, Proteomics, protein, mirna prediction
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/40
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

canuConcurrency
type: FormalParameter
conforms to: 1.0-RELEASE

corMaxEvidenceErate
type: FormalParameter
conforms to: 1.0-RELEASE

database
type: FormalParameter
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diploidOrganism
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conforms to: 1.0-RELEASE

falseValue
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fix
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genomeSize
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illuminaClip
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leading
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maxFragmentLens
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minReadLen
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minThreads
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minlen
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orientation
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pacBioInBam
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prefix
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readsPe2
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repBaseLibrary
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taxons
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threads
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trueValue
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assemblyMerged
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Common Workflow Language
type: ComputerLanguage
alternate name: CWL

Assemblosis
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Pasi Korhonen
date created: 2022-04-20T23:10:26Z
date modified: 2023-01-16T13:59:43Z
description: ## CWL based workflow to assemble haploid/diploid eukaryote genomes of non-model organisms The workflow is designed to use both PacBio long-reads and Illumina short-reads. The workflow first extracts, corrects, trims and decontaminates the long reads. Decontaminated trimmed reads are then used to assemble the genome and raw reads are used to polish it. Next, Illumina reads are cleaned and used to further polish the resultant assembly. Finally, the polished assembly is masked using inferred repeats and haplotypes are eliminated. The workflow uses BioConda and DockerHub to install required software and is therefore fully automated. In addition to final assembly, the workflow produces intermediate assemblies before and after polishing steps. The workflow follows the syntax for CWL v1.0. ### Dependencies # Programs The pipeline can be run either using [Cromwell](https://cromwell.readthedocs.io/en/stable) or [cwltool reference](https://github.com/common-workflow-language/cwltool) implementation and docker containers can be run either using [Singularity](https://singularity.lbl.gov) or [udocker](https://singularity.lbl.gov). Cromwell implementation * [cromwell v44](https://github.com/broadinstitute/cromwell/releases/tag/44) * [java-jdk v8.0.112](https://www.java.com/en) Reference implementation * [cwltool v1.0.20181012180214](https://github.com/common-workflow-language/cwltool) * [nodejs v10.4.1 required by cwltool](https://nodejs.org/en) * [Python library galaxy-lib v18.5.7](https://pypi.org/project/galaxy-lib) Singularity software packages have to be installed server-wide by administrator * [Singularity v3.2.1](https://singularity.lbl.gov) * [squashfs-tools v4.3.0](https://github.com/plougher/squashfs-tools) Udocker software package can be installed locally * [udocker v1.1.2](https://github.com/indigo-dc/udocker) # Data * [Illumina adapters converted to FASTA format](http://sapac.support.illumina.com/downloads/illumina-adapter-sequences-document-1000000002694.html) * [N…
input: canuConcurrency
input: corMaxEvidenceErate
input: database
input: diploidOrganism
input: falseValue
input: fix
input: genomeSize
input: illuminaClip
input: leading
input: maxFragmentLens
input: minReadLen
input: minThreads
input: minlen
input: orientation
input: pacBioDataDir
input: pacBioInBam
input: pacBioTmpDir
input: partialMatch
input: phredsPe
input: polishedAssembly
input: prefix
input: readsPe1
input: readsPe2
input: repBaseLibrary
input: slidingWindow
input: taxons
input: threads
input: trailing
input: trueValue
license: BSD-3-Clause
output: arrowAssembly
output: assemblyMasked
output: assemblyMerged
output: canuAssembly
output: contaminatedReads
output: correctedReads
output: deconClassification
output: deconReport
output: decontaminatedReads
output: pilonAssembly
output: sortedBamIndexFileOut
output: trimmedReadFiles1
output: trimmedReadFiles2
output: trimmedReads
producer: https://workflowhub.eu/projects/90
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Workflow Description Language
type: ComputerLanguage
alternate name: WDL

Escalibur
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2022-04-20T23:21:34Z
date modified: 2023-01-16T13:59:45Z
description: # ESCALIBUR Escalibur Population Genomic Analysis Pipeline is able to explore key aspects centering the population genetics of organisms, and automates three key bioinformatic components in population genomic analysis using Workflow Definition Language (WDL: https://openwdl.org/), and customised R, Perl, Python and Unix shell scripts. Associated programs are packaged into a platform independent singularity image, for which the definition file is provided. The workflow for analysis using Escalibur consists of three steps - each step can be run in a separate workflow in a sequential manner; step 2 is optional. 1. Trimming and mapping the raw data - selection of the best reference genome; 2. Removing the contamination from mapped data; 3. Recalibration, variant calling and filtering; This implementation runs both locally and in a distributed environment that uses SLURM job scheduler. ## Dependencies Following software dependencies are required: * Git * SLURM scheduler required for distributed HPC environment (https://slurm.schedmd.com/documentation.html) * Python3.7: (https://www.python.org/) * Perl 5.26.2: (https://www.perl.org/) * Java 1.8 * Singularity 3.7.3: (https://sylabs.io/singularity/) ## Step 1: Installation Typically, the installation of Singularity requires root rights. You should therefore contact your administrator to get it correctly installed. Minimum Linux kernel version requirement is 3.8, thought >= 3.18 would be preferred (https://sylabs.io/guides/3.5/admin-guide/installation.html). Clone the git repository to a directory on your cluster or stand-alone server. ``` > git clone --depth 1 -b v0.3-beta https://gitlab.unimelb.edu.au/bioscience/escalibur.git > cd escalibur ``` ### Description of Files * `workflow-main.local.config`: main configuration file for stand alone server runtime environment * `workflow-main.slurm.config`: main configuration file for HPC runtime environment that support Slurm job scheduler * `workflow-mapping.json`: defines locatio…
license: BSD-3-Clause
producer: https://workflowhub.eu/projects/90
programming language: Workflow Description Language
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

Deduplicate reads
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conforms to: 1.0-RELEASE

Output Destination
type: FormalParameter
conforms to: 1.0-RELEASE

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Short read quality control, trimming and contamination filter
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/46
creator: https://workflowhub.eu/people/653
creator: https://workflowhub.eu/people/84
date created: 2025-11-06T13:41:16Z
date modified: 2025-11-06T16:37:28Z
description: **Workflow for short paired end reads quality control, trimming and filtering.** Multiple paired datasets will be merged into single paired dataset. Summary: - Sequali QC on raw data files - fastp for read quality trimming - BBduk for phiX and rRNA filtering (optional) - Filter human reads using Hostile (optional) - Custom read filtering using Hostile (optional) - Sequali QC on filtered (merged) data Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default **All tool CWL files and other workflows can be found at:** https://gitlab.com/m-unlock/cwl **How to setup and use an UNLOCK workflow:** https://docs.m-unlock.nl/docs/workflows/setup.html
input: Deduplicate reads
input: Output Destination
input: Don't output reads.
input: filter rRNA
input: Forward reads
input: Filter human reads
input: Identifier
input: Keep mapped reads
input: Maximum memory in MB
input: Filter reference file(s)
input: Reverse reads
input: Skip QC filtered
input: Skip QC unfiltered
input: Input URLs used for this run
input: Number of threads
is based on: https://workflowhub.eu/workflows/336
keywords: illumina, Genomics, Transcriptomics, quality, filtering, Classification
license: Apache-2.0
output: Filtered forward read
output: Filtered reverse read
output: Filtering reports folder
producer: https://workflowhub.eu/projects/16
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 2

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type: ComputerLanguage
alternate name: CWL

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conforms to: 1.0-RELEASE

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LongRead Quality Control and Filtering
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/227
creator: https://workflowhub.eu/people/46
creator: https://workflowhub.eu/people/84
date created: 2022-04-21T16:19:59Z
date modified: 2023-04-07T14:07:55Z
description: ### Workflow for LongRead Quality Control and Filtering - NanoPlot (read quality control) before and after filtering - Filtlong (read trimming) - Kraken2 taxonomic read classification before and after filtering - Minimap2 read filtering based on given references Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default **All tool CWL files and other workflows can be found here:** https://gitlab.com/m-unlock/cwl/workflows **How to setup and use an UNLOCK workflow:** https://m-unlock.gitlab.io/docs/setup/setup.html
input: Output Destination
input: Contamination reference file
input: identifier used
input: Keep mapped reads
input: Kraken2 database
input: Maximum memory in MB
input: Nanopore reads
input: CWL base step number
input: Number of threads
keywords: Genomics, nanopore, CWL, Assembly
license: Apache-2.0
output: Filtered nanopore reads
output: Filtering reports folder
producer: https://workflowhub.eu/projects/16
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

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conforms to: 1.0-RELEASE

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featureCounts on input dataset(s): Summary
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Galaxy
type: ComputerLanguage

Biomarker screening in preeclampsia
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Marlene Rezk
date created: 2022-05-03T13:05:01Z
date modified: 2023-02-13T14:06:45Z
description: Objective. Biomarkers have become important for the prognosis and diagnosis of various diseases. High-throughput methods such as RNA-sequencing facilitate the detection of differentially expressed genes (DEGs), hence potential biomarker candidates. Individual studies suggest long lists of DEGs, hampering the identification of clinically relevant ones. Concerning preeclampsia, a major obstetric burden with high risk for adverse maternal and/or neonatal outcomes, limitations in diagnosis and prediction are still important issues. Therefore, we developed a workflow to facilitate the screening for biomarkers. Methods. Based on the tool DeSeq2, we established a comprehensive workflow for the identification of DEGs, analyzing data from multiple publicly available RNA-sSequencing studies. We applied it to four RNA-sSequencing datasets (one blood, three placenta) analyzing patients with preeclampsia and normotensive controls. We compared our results with other published approaches and evaluated their performance. Results. We identified 110 genes dysregulated in preeclampsia, observed in ≥3 of the analyzed studies, six even in all four studies. Among them were FLT-1, TREM-1, and FN1 which either represent established biomarkers on protein level, or promising candidates based on recent studies. In comparison, using a published meta-analysis approach we obtained 5,240 DEGs. Conclusions. We present a data analysis workflow for preeclampsia biomarker screening, capable of identifying significant biomarker candidates, while drastically decreasing the numbers of candidates. Moreover, we were also able to confirm its performance for heart failure. Our approach can be applied to additional diseases for biomarker identification and the set of identified DEGs in preeclampsia represents a resource for further studies.
license: CC-BY-4.0
output: Bowtie2 on input dataset(s): alignments
output: DESeq2 plots on input dataset(s)
output: DESeq2 result file on input dataset(s)
output: FastQC on input dataset(s): RawData
output: FastQC on input dataset(s): Webpage
output: _anonymous_output_1
output: _anonymous_output_2
output: featureCounts on input dataset(s): Counts
output: featureCounts on input dataset(s): Summary
producer: Gyn Department
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Shell Script
type: ComputerLanguage

GermlineShortV_biovalidation
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Nandan Deshpande
creator: https://workflowhub.eu/people/123
creator: https://workflowhub.eu/people/141
creator: https://workflowhub.eu/people/258
date created: 2022-05-05T05:02:08Z
date modified: 2023-01-16T13:59:52Z
description: # GermlineShortV_biovalidation - [Description](#description) - [Diagram](#diagram) - [User guide](#user-guide) - [Quick start guide](#quick-start-guide) - [Benchmarking](#benchmarking) - [Workflow summaries](#workflow-summaries) - [Metadata](#metadata) - [Component tools](#component-tools) - [Required (minimum) inputs/parameters](#required-minimum-inputsparameters) [Preparing your own input files](#preparing-input-files) - [Additional notes](#additional-notes) - [Understanding your outputs](#understanding-your-outputs) - [Performance metrics explained](#performance-metrics-explained) - [Help/FAQ/Troubleshooting](#helpfaqtroubleshooting) - [Acknowledgements/citations/credits](#acknowledgementscitationscredits) ## Description Population-scale WGS cohorts are essential resources for genetic analyses including heritable diseases, evolutionary genomics, conservation biology, and population genomics. Processing raw reads into analysis-ready variants remains challenging. Various mapping and variant calling pipelines have been made publicly available in recent decades. Designing a mapping and variant calling pipeline to meet your needs is dependent on the compute infrastructure you’re working on, the types of variants you’re primarily interested in, and the sequencing technology you use to generate raw sequencing data. Keep in mind that the tools you use to build your pipeline can affect variant calling accuracy. Further, optimisation and customisation of these tools’ commands can also affect their performance. Best-practice recommendations for variant calling pipelines vary dramatically between species and research questions, depending on the availability of genomic resources for the population of interest, genome structure, and clinical relevance of the resulting variant dataset. It is important to not only design a robust variant calling pipeline but also fine-tune it to achieve optimal performance for your dataset and research question. There are various measurements th…
is part of: https://workflowhub.eu/collections/6
license: GPL-3.0
producer: https://workflowhub.eu/projects/43
programming language: Shell Script
sd publisher: about.workflowhub.eu
version: 1

Genomic variants - SNPs and INDELs detection using GATK4 spark based tools.
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/26
date created: 2020-06-17T06:38:32Z
date modified: 2023-01-16T13:42:33Z
description: Author: AMBARISH KUMAR er.ambarish@gmail.com & ambari73_sit@jnu.ac.in This is a proposed standard operating procedure for genomic variant detection using GATK4. It is hoped to be effective and useful for getting SARS-CoV-2 genome variants. It uses Illumina RNASEQ reads and genome sequence.
input: rnaseq_left_reads
input: rnaseq_right_reads
input: sampleName
input: sars_cov_2_reference_2bit_genome
input: sars_cov_2_reference_genome
keywords: CWL, GATK4, SNPs, INDELs, SPARK
license: Apache-2.0
output: indels
output: snps
producer: https://workflowhub.eu/projects/6
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

rnaseq_left_reads
type: FormalParameter
conforms to: 1.0-RELEASE

rnaseq_right_reads
type: FormalParameter
conforms to: 1.0-RELEASE

sample_name
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sars_cov_2_reference_genome
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indel
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snp
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Nextflow
type: ComputerLanguage

HiFi de novo genome assembly workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2022-05-10T23:42:38Z
date modified: 2023-01-16T13:59:52Z
description: # HiFi *de novo* genome assembly workflow HiFi-assembly-workflow is a bioinformatics pipeline that can be used to analyse Pacbio CCS reads for *de novo* genome assembly using PacBio Circular Consensus Sequencing (CCS) reads. This workflow is implemented in Nextflow and has 3 major sections. Please refer to the following documentation for detailed description of each workflow section: - [Pre-assembly quality control (QC)](https://github.com/AusARG/hifi-assembly-workflow/blob/master/recommendations.md#stage-1-pre-assembly-quality-control) - [Assembly](https://github.com/AusARG/hifi-assembly-workflow/blob/master/recommendations.md#stage-2-assembly) - [Post-assembly QC](https://github.com/AusARG/hifi-assembly-workflow/blob/master/recommendations.md#stage-3-post-assembly-quality-control) ## HiFi assembly workflow flowchart ![](https://github.com/AusARG/hifi-assembly-workflow/blob/master/workflow.png?raw=true) # Quick Usage: The pipeline has been tested on NCI Gadi and AGRF balder cluster. If needed to run on AGRF cluster, please contact us at bioinformatics@agrf.org.au. Please note for running this on NCI Gadi you need access. Please refer to Gadi guidelines for account creation and usage: these can be found at https://opus.nci.org.au/display/Help/Access. Here is an example that can be used to run a phased assembly on Gadi: ``` Module load nextflow/21.04.3 nextflow run Hifi_assembly.nf –bam_folder -profile gadi The workflow accepts 2 mandatory arguments: --bam_folder -- Full Path to the CCS bam files -profile -- gadi/balder/local ``` Please note that you can either run jobs interactively or submit jobs to the cluster. This is determined by the -profile flag. By passing the gadi tag to the profile argument, the jobs are submitted and run on the cluster. # General recommendations for using the HiFi *de novo* genome assembly workflow ## Example local profile usage ``` Start a screen, submit a job, and run the workflow Screen -S ‘name’ qsub -I -qnormal -Pwz54 -lwalltime=48:0…
license: CC-BY-4.0
producer: AGRF BIO
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

KNIME
type: ComputerLanguage

MaCProQC
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Julian Uszkoreit
date created: 2022-05-20T09:38:29Z
date modified: 2023-01-16T13:59:52Z
description: A workflow for the quality assessment of mass spectrometry (MS) based proteomics analyses
license: BSD-3-Clause
producer: Medizinisches Proteom-Center, Medical Bioinformatics
programming language: KNIME
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

Reference filters files
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Kallisto RNAseq Workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/46
creator: https://workflowhub.eu/people/84
date created: 2022-05-20T10:10:26Z
date modified: 2023-01-16T13:59:53Z
description: ### Workflow Kallisto RNAseq **(pseudoalignment on transcripts)** - Workflow Illumina Quality: https://workflowhub.eu/workflows/336?version=1 - kallisto **All tool CWL files and other workflows can be found here:** Tools: https://git.wur.nl/unlock/cwl/-/tree/master/cwl Workflows: https://git.wur.nl/unlock/cwl/-/tree/master/cwl/workflows **How to setup and use an UNLOCK workflow:** https://m-unlock.gitlab.io/docs/setup/setup.html
input: Reference filters files
input: Output Destination
input: Filter rRNA
input: Forward reads
input: Identifier
input: kallisto index file
input: Maximum memory in MB
input: Reverse reads
input: Threads
license: Apache-2.0
output: Filtered statistics
output: kallisto output
producer: https://workflowhub.eu/projects/16
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

Demultiplexed reads
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conforms to: 1.0-RELEASE

Cutadapt on input dataset(s): Read 1 Output
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FastQC on input dataset(s): Webpage
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MultiQC on input dataset(s): Webpage
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QC of RADseq reads
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/200
date created: 2022-05-31T06:55:21Z
date modified: 2023-01-30T18:21:31Z
description: # workflow-qc-of-radseq-reads These workflows are part of a set designed to work for RAD-seq data on the Galaxy platform, using the tools from the Stacks program. Galaxy Australia: https://usegalaxy.org.au/ Stacks: http://catchenlab.life.illinois.edu/stacks/ ## Inputs * demultiplexed reads in fastq format, in a collection * two adapter sequences in fasta format, for input into cutadapt ## Steps and outputs The workflow can be modified to suit your own parameters. The workflow steps are: * Run FastQC to get statistics on the raw reads, send to MultiQC to create a nice output. This is tagged as "Report 1" in the Galaxy history. * Run Cutadapt on the reads to cut adapters - enter two files with adapter sequence at the workflow option for "Choose file containing 3' adapters". The default settings are on except that the "Maximum error rate" for the adapters is set to 0.2 instead of 0.1. Send output statistics to MulitQC, this is "Report 2" in the Galaxy history. Note that you may have different requirements here in terms of how many adapter sequences you want to enter. We recommend copying the workflow and modifying as needed. * Send these reads to fastp for additional filtering or trimming. Default settings are on but can be modified as needed. Send output statistics to MultiQC, this is "Report 3" in the Galaxy history. * The filtered and trimmed reads are then ready for the stacks workflows. ![qc-wf](wf-image-qc.png)
input: Demultiplexed reads
is part of: https://workflowhub.eu/collections/6
license: Apache-2.0
output: Cutadapt on input dataset(s): Read 1 Output
output: FastQC on input dataset(s): RawData
output: FastQC on input dataset(s): Webpage
output: MultiQC on input dataset(s): Stats
output: MultiQC on input dataset(s): Webpage
output: _anonymous_output_1
output: _anonymous_output_2
output: _anonymous_output_3
output: _anonymous_output_4
output: _anonymous_output_5
output: _anonymous_output_6
output: fastp on input dataset(s): HTML report
output: fastp on input dataset(s): Read 1 output
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

Stacks RAD-seq reference-guided workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/200
date created: 2022-05-31T07:20:38Z
date modified: 2023-01-30T18:21:31Z
description: # workflow-ref-guided-stacks These workflows are part of a set designed to work for RAD-seq data on the Galaxy platform, using the tools from the Stacks program. Galaxy Australia: https://usegalaxy.org.au/ Stacks: http://catchenlab.life.illinois.edu/stacks/ ## Inputs * demultiplexed reads in fastq format, may be output from the QC workflow. Files are in a collection. * population map in text format * reference genome in fasta format ## Steps and outputs BWA MEM 2: * The reads are mapped to the reference genome; output in BAM format * The collection of bam files is named something like Map with BWA-MEM on collection 5 (mapped reads in BAM format) * Each of the bam files in the collection is named something like sample_CAAC Samtools stats before filtering: * These bam files are sent to Samtools stats to get statistics; these are then sent to MultiQC to provide a nice output. This is tagged as "bam stats before filtering" in the Galaxy history. * The "General Statistics" show how many reads were mapped - if there is a low mapping rate, it may be worth re-checking or repeating QC on the raw reads, or considering a different reference genome, or using a de novo approach. To see if many reads have been soft-clipped by Bwa mem (which may affect how well gstacks can work), look at the "Alignment Metrics" section, and the row with "Mapped bases (Cigar)". Hover over the dots to see sample names especially towards the left of the row - these have the least mapped reads. Samtools view: * This step filters out certain reads from the bam files. The default settings are to exclude reads if they are unmapped, if the alignment is not primary or is supplementary, if the read fails platform/vendor quality checks, and if the read is a PCR or optical duplicate. * The output bams are tagged with "filtered bams" in the Galaxy history. Samtools stats after filtering: * Filtered bams are sent again to samtools stats, and statistics to MultiQC, with the report tagged as "bam stats after filt…
is part of: https://workflowhub.eu/collections/6
license: Apache-2.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

Stacks RAD-seq de novo workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/200
date created: 2022-05-31T07:39:10Z
date modified: 2023-01-30T18:21:31Z
description: # workflow-denovo-stacks These workflows are part of a set designed to work for RAD-seq data on the Galaxy platform, using the tools from the Stacks program. Galaxy Australia: https://usegalaxy.org.au/ Stacks: http://catchenlab.life.illinois.edu/stacks/ ## Inputs * demultiplexed reads in fastq format, may be output from the QC workflow. Files are in a collection. * population map in text format ## Steps and outputs ustacks: * input reads go to ustacks. * ustacks assembles the reads into matching stacks (hypothetical alleles). * The outputs are in a collection called something like: Stacks2: ustacks on data 21, data 20, and others Loci and polymorphism. Click on this to see the files: * for each sample, assembled loci (tsv format), named e.g. sample_CAAC.tags * for each sample, model calls from each locus (tsv format), named e.g. sample_CAAC.snps * for each sample, haplotypes/alleles recorded from each locus (tsv format), named e.g. sample_CAAC.alleles * Please see sections 6.1 to 6.4 in https://catchenlab.life.illinois.edu/stacks/manual/#ufiles for a full description. cstacks: * cstacks will merge stacks into a catalog of consensus loci. * The outputs are in a collection called something like Stacks2: cstacks on data 3, data 71, and others Catalog of loci. Click on this to see the three files, each in tsv format: catalog.tags catalog.snps catalog.alleles sstacks: * sstacks will compare each sample to the loci in the catalog. * The outputs are in a collection called something like Stacks2: sstacks on data 3, data 76, and others Matches to the catalog.Click on this to see the files: There is one file for each sample, named e.g. sample_CAAC.matches, in tsv format. tsv2bam: * Conversion to BAM format * Reads from each sample are now aligned to each locus, and the tsv2bam tool will convert this into a bam file for each sample. * The outputs are in a collection called something like Stacks2: tsv2bam on data 3, data 94, and others Matches to the catalog.Click on this to se…
is part of: https://workflowhub.eu/collections/6
license: Apache-2.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

Partial de novo workflow: ustacks only
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/200
date created: 2022-05-31T07:50:13Z
date modified: 2023-01-30T18:21:31Z
description: # workflow-partial-ustacks-only These workflows are part of a set designed to work for RAD-seq data on the Galaxy platform, using the tools from the Stacks program. Galaxy Australia: https://usegalaxy.org.au/ Stacks: http://catchenlab.life.illinois.edu/stacks/ For the full de novo workflow see https://workflowhub.eu/workflows/348 You may want to run ustacks with different batches of samples. * To be able to combine these later, there are some necessary steps - we need to keep track of how many samples have already run in ustacks, so that new samples can be labelled with different identifying numbers. * In ustacks, under "Processing options" there is an option called "Start identifier at". * The default for this is 1, which can be used for the first batch of samples. These will then be labelled as sample 1, sample 2 and so on. * For any new batches of samples to process in ustacks, we will want to start numbering these at the next available number. e.g. if there were 10 samples in batch 1, this should then be set to start at 11. To combine multiple outputs from ustacks, providing these have been given appropriate starting identifiers: * Find the ustacks output in the Galaxy history. This will be a list of samples. * Click on the cross button next to the filename to delete, but select "Collection only". This releases the items from the list, but they will now be hidden in the Galaxy history. * In the history panel, click on "hidden" to reveal any hidden files. Unhide the samples. * Do this for all the batches of ustacks outputs that are needed. * Click on the tick button, tick all the samples needed, then "For all selected" choose "Build dataset list" * This is now a combined set of samples for input into cstacks.
is part of: https://workflowhub.eu/collections/6
license: Apache-2.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Genomic variants - SNPs and INDELs detection using SAMTools.
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-06-17T07:41:06Z
date modified: 2023-01-16T13:42:36Z
description: Author: AMBARISH KUMAR er.ambarish@gmail.com; ambari73_sit@jnu.ac.in This is a proposed standard operating procedure for genomic variant detection using SAMTools. It is hoped to be effective and useful for getting SARS-CoV-2 genome variants. It uses Illumina RNASEQ reads and genome sequence.
input: rnaseq_left_reads
input: rnaseq_right_reads
input: sample_name
input: sars_cov_2_reference_genome
keywords: CWL, SAMTools, SNPs, INDELs, covid-19
license: Apache-2.0
output: indel
output: snp
producer: https://workflowhub.eu/projects/6
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

List of Illumina accessions
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Partial de novo workflow: c-s-g-pops only
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/200
date created: 2022-05-31T07:56:39Z
date modified: 2023-01-30T18:21:31Z
description: # workflow-partial-cstacks-sstacks-gstacks These workflows are part of a set designed to work for RAD-seq data on the Galaxy platform, using the tools from the Stacks program. Galaxy Australia: https://usegalaxy.org.au/ Stacks: http://catchenlab.life.illinois.edu/stacks/ This workflow takes in ustacks output, and runs cstacks, sstacks and gstacks. To generate ustacks output see https://workflowhub.eu/workflows/349 For the full de novo workflow see https://workflowhub.eu/workflows/348
input: Population map
input: Ustacks inputs
is part of: https://workflowhub.eu/collections/6
license: Apache-2.0
output: Stacks2: cstacks on input dataset(s) Catalog of loci
output: Stacks2: cstacks on input dataset(s) log file
output: Stacks2: gstacks on input dataset(s) Assembled contigs and variant sites
output: Stacks2: gstacks on input dataset(s) log file
output: Stacks2: populations on input dataset(s) Population-level haplotype summary statistics
output: Stacks2: populations on input dataset(s) Population-level summary statistics
output: Stacks2: populations on input dataset(s) Populations log distributions
output: Stacks2: populations on input dataset(s) Raw Genotypes/Haplotypes
output: Stacks2: populations on input dataset(s) Summary of Population-level summary statistics
output: Stacks2: populations on input dataset(s) log file
output: Stacks2: sstacks on input dataset(s) Matches to the catalog
output: Stacks2: sstacks on input dataset(s) log file
output: Stacks2: tsv2bam on input dataset(s) Matches to the catalog (bam)
output: Stacks2: tsv2bam on input dataset(s) log file
output: _anonymous_output_2
output: _anonymous_output_3
output: _anonymous_output_4
output: _anonymous_output_5
output: _anonymous_output_6
output: _anonymous_output_7
output: _anonymous_output_8
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
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Partial ref-guided workflow - bwa mem only
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/200
date created: 2022-05-31T08:05:01Z
date modified: 2023-01-30T18:21:31Z
description: # workflow-partial-bwa-mem These workflows are part of a set designed to work for RAD-seq data on the Galaxy platform, using the tools from the Stacks program. Galaxy Australia: https://usegalaxy.org.au/ Stacks: http://catchenlab.life.illinois.edu/stacks/ This workflow is part of the reference-guided stacks workflow, https://workflowhub.eu/workflows/347 Inputs * demultiplexed reads in fastq format, may be output from the QC workflow. Files are in a collection. * reference genome in fasta format Outputs * A set of filtered bam files, ready for the next part of the stacks workflow (e.g. gstacks). * Statistics on the bam files.
is part of: https://workflowhub.eu/collections/6
license: Apache-2.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

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Population map
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Stacks2: gstacks on input dataset(s) log file
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Partial ref-guided workflow - gstacks and pops
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/200
date created: 2022-05-31T08:12:30Z
date modified: 2023-01-30T18:21:31Z
description: # workflow-partial-gstacks-populations These workflows are part of a set designed to work for RAD-seq data on the Galaxy platform, using the tools from the Stacks program. Galaxy Australia: https://usegalaxy.org.au/ Stacks: http://catchenlab.life.illinois.edu/stacks/ This workflow is part of the reference-guided stacks workflow, https://workflowhub.eu/workflows/347 This workflow takes in bam files and a population map. To generate bam files see: https://workflowhub.eu/workflows/351
input: Bam files
input: Population map
is part of: https://workflowhub.eu/collections/6
license: Apache-2.0
output: Stacks2: gstacks on input dataset(s) Assembled contigs and variant sites
output: Stacks2: gstacks on input dataset(s) log file
output: Stacks2: populations on input dataset(s) Population-level haplotype summary statistics
output: Stacks2: populations on input dataset(s) Population-level summary statistics
output: Stacks2: populations on input dataset(s) Populations log distributions
output: Stacks2: populations on input dataset(s) Raw Genotypes/Haplotypes
output: Stacks2: populations on input dataset(s) Summary of Population-level summary statistics
output: Stacks2: populations on input dataset(s) log file
output: _anonymous_output_1
output: _anonymous_output_2
output: _anonymous_output_3
output: _anonymous_output_4
output: _anonymous_output_5
output: _anonymous_output_6
output: _anonymous_output_7
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Wolfgang Maier
type: Person

Galaxy
type: ComputerLanguage

GenBank genome
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Paired Collection
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Fasta sequences for
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Map with BWA-MEM on input dataset(s) (mapped reads in BAM format)
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SnpEff eff: on input dataset(s) - stats
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SnpEff4.3 database for
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_anonymous_output_1
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called_variants
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fastp_html_report
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fastp_pe
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markduplicates_stats
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preprocessing_and_mapping_reports
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realigned_deduplicated_filtered_mapped_reads_with_indel_quals
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soft_filtered_variants
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Generic variation analysis on WGS PE data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Wolfgang Maier
date created: 2022-06-01T12:46:37Z
date modified: 2023-02-13T14:06:44Z
description: # Generic variant calling A generic workflow for identification of variants in a haploid genome such as genomes of bacteria or viruses. It can be readily used on MonkeyPox. The workflow accepts two inputs: - A genbank file with the reference genomes - A collection of paired fastqsanger files The workflow outputs a collection of VCF files for each sample (each fastq pair). These VCF files serve as input to the [Reporting workflow](https://workflowhub.eu/workflows/354). Workflow can be accessed directly on [usegalaxy.org](https://usegalaxy.org/u/aun1/w/generic-variation-analysis-on-wgs-pe-data) The general idea of the workflow is: ![](https://i.imgur.com/rk40Y4t.png)
input: GenBank genome
input: Paired Collection
keywords: mpxv, generic
license: CC-BY-4.0
output: Fasta sequences for
output: Map with BWA-MEM on input dataset(s) (mapped reads in BAM format)
output: SnpEff eff: on input dataset(s) - stats
output: SnpEff4.3 database for
output: _anonymous_output_1
output: called_variants
output: fastp_html_report
output: fastp_pe
output: filtered_mapped_reads
output: mapped_reads_stats
output: markduplicates_reads
output: markduplicates_stats
output: preprocessing_and_mapping_reports
output: realigned_deduplicated_filtered_mapped_reads
output: realigned_deduplicated_filtered_mapped_reads_with_indel_quals
output: soft_filtered_variants
producer: https://workflowhub.eu/projects/24
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Wolfgang Maier
type: Person

Galaxy
type: ComputerLanguage

AF Filter
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DP_ALT Filter
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Variation data to report
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af_recalculated
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all_variants_all_samples
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by_variant_report
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cleaned_header
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collapsed_effects
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combined_variant_report
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filtered_extracted_variants
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filtered_variants
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highest_impact_effects
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prefiltered_variants
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variants_for_plotting
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Generic variation analysis reporting
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Wolfgang Maier
date created: 2022-06-01T15:36:06Z
date modified: 2023-01-16T14:00:24Z
description: # Generic variation analysis reporting This workflow generates reports from a list of variants generated by [Variant Calling Workflow](https://workflowhub.eu/workflows/353). The workflow accepts a single input: - A collection of VCF files The workflow produces two outputs (format description below): 1. A list of variants grouped by Sample 2. A list of variants grouped by Variant Here is example of output **by sample**. In this table all varinats in all samples are epxlicitrly listed: | Sample | POS | FILTER | REF | ALT | DP | AF | AFcaller | SB | DP4 | IMPACT | FUNCLASS | EFFECT | GENE | CODON | AA | TRID | min(AF) | max(AF) | countunique(change) | countunique(FUNCLASS) | change | |----------|------|----------|---------|-----|-----|------|-----------|-----|-------|----------|---------------|-------------|--------|-------------| ---|--------|----------|-----------|-------------------------|------------------------------|------------| | ERR3485786 | 11644 | PASS | A | G | 97 | 0.979381 | 0.907216 | 0 | 1,1,49,46 | LOW | SILENT | SYNONYMOUS_CODING | D7L | tgT/tgC | C512 | AKG51361.1 | 0.979381 | 1 | 1 | 1 | A>G | | ERR3485786 | 11904 | PASS | T | C | 102 | 0.990196 | 0.95098 | 0 | 0,0,51,50 | MODERATE | MISSENSE | NON_SYNONYMOUS_CODING | D7L | Act/Gct | T426A | AKG51361.1 | 0.990196 | 1 | 1 | 1 | T>C | > **Note** the two alernative allele frequency fields: "AFcaller" ans "AF". LoFreq reports AF values listed in "AFcaller". They incorrect due to the known LoFreq [bug](https://github.com/CSB5/lofreq/issues/80). To correct for this we are recomputing AF values from DP4 and DP fields as follows: `AF == (DP4[2] + DP4[3]) / DP.` Here is an example of output **by variant**. In this table data is aggregated by variant across all samples in which this variant is present: | POS | REF | ALT | IMPACT | FUNCLASS | EFFECT | GENE | CODON | AA | TRID | countunique(Sample) | min(AF) | max(AF) | SAMPLES(above-thresholds) | SAMPLES(all) | AFs(all) | change | |-----|-------|-----|--------…
input: AF Filter
input: DP Filter
input: DP_ALT Filter
input: Variation data to report
keywords: mpvx, generic
license: CC-BY-4.0
output: af_recalculated
output: all_variants_all_samples
output: by_variant_report
output: cleaned_header
output: collapsed_effects
output: combined_variant_report
output: filtered_extracted_variants
output: filtered_variants
output: highest_impact_effects
output: prefiltered_variants
output: processed_variants_collection
output: variants_for_plotting
producer: https://workflowhub.eu/projects/24
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Wolfgang Maier
type: Person

Galaxy
type: ComputerLanguage

Depth-threshold for masking
type: FormalParameter
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Reference genome
type: FormalParameter
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Variant calls
type: FormalParameter
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aligned reads data for depth calculation
type: FormalParameter
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min-AF for consensus variant
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min-AF for failed variants
type: FormalParameter
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1_based_masking_regions
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called_variant_sites
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chrom_pos_ref_called_variants
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chrom_pos_ref_called_variants_with_0_based_start
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chrom_pos_ref_called_variants_with_0_based_start_end
type: FormalParameter
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chrom_pos_ref_failed_variants
type: FormalParameter
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chrom_pos_ref_failed_variants_with_0_based_start
type: FormalParameter
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chrom_pos_ref_failed_variants_with_0_based_start_end
type: FormalParameter
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consensus
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consensus_variants
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coverage_depth
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failed_variant_sites
type: FormalParameter
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filter_failed_variants
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low_cov_regions
type: FormalParameter
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low_cov_regions_plus_filter_failed
type: FormalParameter
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low_cov_regions_plus_filter_failed_combined
type: FormalParameter
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masking_regions
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masking_regions_with_1_based_start
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multisample_consensus_fasta
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Generic consensus construction from VCF calls
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Wolfgang Maier
date created: 2022-06-03T09:33:10Z
date modified: 2023-02-13T14:06:44Z
description: # Generic consensus building This workflow generates consensus sequences using a list of variants generated by [Variant Calling Workflow](https://workflowhub.eu/workflows/353). The workflow accepts a single input: - A collection of VCF files The workflow produces a single output: - Consensus sequence for each input VCF file The workflow can be accessed at [usegalaxy.org](https://usegalaxy.org/u/aun1/w/consensus-construction)
input: Depth-threshold for masking
input: Reference genome
input: Variant calls
input: aligned reads data for depth calculation
input: min-AF for consensus variant
input: min-AF for failed variants
keywords: mlxv, generic
license: CC-BY-4.0
output: 1_based_masking_regions
output: called_variant_sites
output: chrom_pos_ref_called_variants
output: chrom_pos_ref_called_variants_with_0_based_start
output: chrom_pos_ref_called_variants_with_0_based_start_end
output: chrom_pos_ref_failed_variants
output: chrom_pos_ref_failed_variants_with_0_based_start
output: chrom_pos_ref_failed_variants_with_0_based_start_end
output: consensus
output: consensus_variants
output: coverage_depth
output: failed_variant_sites
output: filter_failed_variants
output: low_cov_regions
output: low_cov_regions_plus_filter_failed
output: low_cov_regions_plus_filter_failed_combined
output: masking_regions
output: masking_regions_with_1_based_start
output: multisample_consensus_fasta
producer: https://workflowhub.eu/projects/24
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Anton Nekrutenko
type: Person

Galaxy
type: ComputerLanguage

#main/GenBank genome
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#main/Name for genome database
type: FormalParameter
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#main/Paired Collection
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#main/Fasta sequences for genbank file
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#main/SnpEff eff: stats
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#main/SnpEff variants
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#main/SnpEff4.3 database
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#main/bwa_mem_alignments
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#main/called_variants
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#main/fastp_html_report
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#main/fastp_pe
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#main/filtered_alignment
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#main/mapped_reads_stats
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#main/markduplicates_reads
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#main/markduplicates_stats
type: FormalParameter
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#main/preprocessing_and_mapping_reports
type: FormalParameter
conforms to: 1.0-RELEASE

#main/realigned_deduplicated_filtered_mapped_reads
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#main/realigned_deduplicated_filtered_mapped_reads_with_indel_quals
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conforms to: 1.0-RELEASE

#main/soft_filtered_variants
type: FormalParameter
conforms to: 1.0-RELEASE

generic-variant-calling-wgs-pe/main
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Anton Nekrutenko
date created: 2022-06-04T02:01:11Z
date modified: 2025-12-12T02:01:33Z
description: Generic variation analysis on WGS PE data ------------------------------------------- This workflows performs paired end read mapping with bwa-mem followed by sensitive variant calling across a wide range of AFs with lofreq and variant annotation with snpEff. The reference genome can be provided as a GenBank file.
input: #main/GenBank genome
input: #main/Name for genome database
input: #main/Paired Collection
keywords: mpxv, generic
license: MIT
output: #main/Fasta sequences for genbank file
output: #main/SnpEff eff: stats
output: #main/SnpEff variants
output: #main/SnpEff4.3 database
output: #main/bwa_mem_alignments
output: #main/called_variants
output: #main/fastp_html_report
output: #main/fastp_pe
output: #main/filtered_alignment
output: #main/mapped_reads_stats
output: #main/markduplicates_reads
output: #main/markduplicates_stats
output: #main/preprocessing_and_mapping_reports
output: #main/realigned_deduplicated_filtered_mapped_reads
output: #main/realigned_deduplicated_filtered_mapped_reads_with_indel_quals
output: #main/soft_filtered_variants
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Miguel Roncoroni
type: Person

Galaxy
type: ComputerLanguage

Input genomes as collection
type: FormalParameter
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ClustalW on input dataset(s): clustal
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conforms to: 1.0-RELEASE

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_anonymous_output_1
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_anonymous_output_2
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_anonymous_output_3
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_anonymous_output_4
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_anonymous_output_5
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_anonymous_output_6
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_anonymous_output_7
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extracted_ORFs
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fasta_header_cleaned
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funannotate_predicted_proteins
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headers_shortened
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proteomes_to_one_file
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repeat_masked
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sample_names_to_headers
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preparing genomic data for phylogeny recostruction (GTN)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Miguel Roncoroni
date created: 2022-06-06T14:05:14Z
date modified: 2023-02-13T14:06:44Z
description: This workflow begins from a set of genome assemblies of different samples, strains, species. The genome is first annotated with Funnanotate. Predicted proteins are furtner annotated with Busco. Next, 'ProteinOrtho' finds orthologs across the samples and makes orthogroups. Orthogroups where all samples are represented are extracted. Orthologs in each orthogroup are aligned with ClustalW. Test dataset: https://zenodo.org/record/6610704#.Ypn3FzlBw5k
input: Input genomes as collection
keywords: phylogenetics, phylogenomics, Annotation
license: CC-BY-4.0
output: ClustalW on input dataset(s): clustal
output: Proteinortho on input dataset(s): orthology-groups
output: Proteinortho_extract_by_orthogroup
output: _anonymous_output_1
output: _anonymous_output_2
output: _anonymous_output_3
output: _anonymous_output_4
output: _anonymous_output_5
output: _anonymous_output_6
output: _anonymous_output_7
output: extracted_ORFs
output: fasta_header_cleaned
output: funannotate_predicted_proteins
output: headers_shortened
output: proteomes_to_one_file
output: repeat_masked
output: sample_names_to_headers
producer: https://workflowhub.eu/projects/97
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Miguel Roncoroni
type: Person

Galaxy
type: ComputerLanguage

Input alignment collection
type: FormalParameter
conforms to: 1.0-RELEASE

A partition file ready for input into RAxML or IQ-tree
type: FormalParameter
conforms to: 1.0-RELEASE

An occupancy file that summarizes the taxon occupancy per sequence
type: FormalParameter
conforms to: 1.0-RELEASE

Astral log.
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conforms to: 1.0-RELEASE

Concatenated fasta alignment file
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conforms to: 1.0-RELEASE

IQ-TREE on input dataset(s): BIONJ Tree
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IQ-TREE on input dataset(s): Consensus Tree
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IQ-TREE on input dataset(s): MaxLikelihood Distance Matrix
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IQ-TREE on input dataset(s): MaxLikelihood Tree
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IQ-TREE on input dataset(s): Occurence Frequencies in Bootstrap Trees
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conforms to: 1.0-RELEASE

IQ-TREE on input dataset(s): Report and Final Tree
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conforms to: 1.0-RELEASE

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Trimmed alignment.
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_anonymous_output_6
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conforms to: 1.0-RELEASE

ML phylogenetic reconstruction
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Miguel Roncoroni
date created: 2022-06-06T14:14:25Z
date modified: 2023-02-13T14:06:44Z
description: Phylogenetic reconstruction using genome-wide and single-gene alignment data. Here we use maximum likelihood reconstruction program IQTree. Data can be prepared using the [phylogenetic data preparation workflow](http://workflowhub.eu/workflows/358) prior to phylogenetic reconstruction. Resulting trees can be viewed interactively using Galaxy's 'Phyloviz' or 'Phylogenetic Tree Visualization'
input: Input alignment collection
keywords: phylogenetics, phylogenomics
license: CC-BY-4.0
output: A partition file ready for input into RAxML or IQ-tree
output: An occupancy file that summarizes the taxon occupancy per sequence
output: Astral log.
output: Concatenated fasta alignment file
output: IQ-TREE on input dataset(s): BIONJ Tree
output: IQ-TREE on input dataset(s): Consensus Tree
output: IQ-TREE on input dataset(s): MaxLikelihood Distance Matrix
output: IQ-TREE on input dataset(s): MaxLikelihood Tree
output: IQ-TREE on input dataset(s): Occurence Frequencies in Bootstrap Trees
output: IQ-TREE on input dataset(s): Report and Final Tree
output: Output tree file
output: Trimmed alignment.
output: _anonymous_output_1
output: _anonymous_output_2
output: _anonymous_output_3
output: _anonymous_output_4
output: _anonymous_output_5
output: _anonymous_output_6
producer: https://workflowhub.eu/projects/97
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

COVID-19: GATK4
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-06-18T15:49:59Z
date modified: 2023-02-13T14:06:46Z
description: Detects SNPs and INDELs.
input: List of Illumina accessions
input: Reference genome annotation
input: Refrence genome
keywords: Galaxy, SNPs, INDELs, GATK4
license: Apache-2.0
output: VCF
output: _anonymous_output_10
output: _anonymous_output_11
output: _anonymous_output_12
output: _anonymous_output_13
output: _anonymous_output_14
output: _anonymous_output_15
output: _anonymous_output_16
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output: _anonymous_output_18
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output: _anonymous_output_20
output: _anonymous_output_21
output: _anonymous_output_22
output: _anonymous_output_23
output: _anonymous_output_24
output: _anonymous_output_25
output: _anonymous_output_4
output: _anonymous_output_5
output: _anonymous_output_6
output: _anonymous_output_7
output: _anonymous_output_8
output: _anonymous_output_9
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

List of Illumina accessions
type: FormalParameter
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Reference genome
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Reference genome annotation
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conforms to: 1.0-RELEASE

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conforms to: 1.0-RELEASE

Galaxy
type: ComputerLanguage

Alex L Mitchell
type: Person

Alex Shlemov
type: Person

Alexandre Almeida
type: Person

Alla Lapidus
type: Person

Anton Korobeynikov
type: Person

Ekaterina Sakharova
type: Person

Guy Cochrane
type: Person

Josephine Burgin
type: Person

Lorna J Richardson
type: Person

Martin Beracochea
type: Person

Maxim Scheremetjew
type: Person

Michael R Crusoe
type: Person

Miguel Boland
type: Person

Olga Kunyavskaya
type: Person

Robert D Finn
type: Person

Simon C Potter
type: Person

Varsha Kale
type: Person

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

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type: FormalParameter
conforms to: 1.0-RELEASE

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conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

HMM_omit_alignment
type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
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type: FormalParameter
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type: FormalParameter
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type: FormalParameter
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contigs
type: FormalParameter
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graphs
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qc-statistics_folder
type: FormalParameter
conforms to: 1.0-RELEASE

qc-status
type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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stats
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conforms to: 1.0-RELEASE

taxonomy-summary_folder
type: FormalParameter
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MGnify - assembly analysis pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Alex L Mitchell
creator: Alex Shlemov
creator: Alexandre Almeida
creator: Alla Lapidus
creator: Anton Korobeynikov
creator: Ekaterina Sakharova
creator: Guy Cochrane
creator: Josephine Burgin
creator: Lorna J Richardson
creator: Martin Beracochea
creator: Maxim Scheremetjew
creator: Michael R Crusoe
creator: Miguel Boland
creator: Olga Kunyavskaya
creator: Robert D Finn
creator: Simon C Potter
creator: Varsha Kale
date created: 2022-06-07T08:03:35Z
date modified: 2023-04-28T10:09:02Z
description: MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations. Documentation: https://docs.mgnify.org/en/latest/analysis.html#assembly-analysis-pipeline
input: 5.8s_pattern
input: 5s_pattern
input: CGC_config
input: CGC_postfixes
input: EggNOG_data_dir
input: EggNOG_db
input: EggNOG_diamond_db
input: HMM_gathering_bit_score
input: HMM_name_database
input: HMM_omit_alignment
input: InterProScan_applications
input: InterProScan_databases
input: InterProScan_outputFormat
input: Uniref90_db_txt
input: cgc_chunk_size
input: clusters_glossary
input: contig_min_length
input: contigs
input: diamond_databaseFile
input: diamond_header
input: diamond_maxTargetSeqs
input: func_ann_names_hmmer
input: func_ann_names_ips
input: go_config
input: gp_flatfiles_path
input: graphs
input: hmmsearch_header
input: ips_header
input: ko_file
input: lsu_db
input: lsu_label
input: lsu_otus
input: lsu_tax
input: other_ncrna_models
input: pathways_classes
input: pathways_names
input: protein_chunk_size_IPS
input: protein_chunk_size_eggnog
input: protein_chunk_size_hmm
input: rfam_model_clans
input: rfam_models
input: ssu_db
input: ssu_label
input: ssu_otus
input: ssu_tax
is based on: https://workflowhub.eu/workflows/360
keywords: Metagenomics, Annotation, workflow, CWL
license: Apache-2.0
output: bgzip_fasta_file
output: bgzip_index
output: chunking_nucleotides
output: chunking_proteins
output: completed_flag_file
output: compressed_files
output: functional_annotation_folder
output: hashsum_input

Alex L Mitchell
type: Person

Alex Shlemov
type: Person

Alexandre Almeida
type: Person

Alla Lapidus
type: Person

Anton Korobeynikov
type: Person

Ekaterina Sakharova
type: Person

Guy Cochrane
type: Person

Josephine Burgin
type: Person

Lorna J Richardson
type: Person

Martin Beracochea
type: Person

Maxim Scheremetjew
type: Person

Michael R Crusoe
type: Person

Miguel Boland
type: Person

Olga Kunyavskaya
type: Person

Robert D Finn
type: Person

Simon C Potter
type: Person

Varsha Kale
type: Person

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

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type: FormalParameter
conforms to: 1.0-RELEASE

5s_pattern
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MGnify - amplicon analysis pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Alex L Mitchell
creator: Alex Shlemov
creator: Alexandre Almeida
creator: Alla Lapidus
creator: Anton Korobeynikov
creator: Ekaterina Sakharova
creator: Guy Cochrane
creator: Josephine Burgin
creator: Lorna J Richardson
creator: Martin Beracochea
creator: Maxim Scheremetjew
creator: Michael R Crusoe
creator: Miguel Boland
creator: Olga Kunyavskaya
creator: Robert D Finn
creator: Simon C Potter
creator: Varsha Kale
date created: 2022-06-07T08:28:11Z
date modified: 2023-01-16T14:01:07Z
description: MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations. Documentation: https://docs.mgnify.org/en/latest/analysis.html#amplicon-analysis-pipeline
input: 5.8s_pattern
input: 5s_pattern
input: forward_reads
input: itsonedb
input: itsonedb_label
input: itsonedb_otu_file
input: itsonedb_tax
input: lsu_db
input: lsu_label
input: lsu_otus
input: lsu_tax
input: qc_min_length
input: reverse_reads
input: rfam_model_clans
input: rfam_models
input: single_reads
input: ssu_db
input: ssu_label
input: ssu_otus
input: ssu_tax
input: stats_file_name
input: unite_db
input: unite_label
input: unite_otu_file
input: unite_tax
keywords: CWL, Metagenomics, rna, workflow
license: Apache-2.0
output: ITS-length
output: completed_flag_file
output: fastp_filtering_json_report
output: gz_files
output: hashsum_paired
output: hashsum_single
output: no_tax_flag_file
output: qc-statistics
output: qc-status
output: qc_summary
output: rna-count
output: sequence-categorisation_folder
output: suppressed_upload
output: taxonomy-summary_folder
producer: https://workflowhub.eu/projects/9
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Alex L Mitchell
type: Person

Alex Shlemov
type: Person

Alexandre Almeida
type: Person

Alla Lapidus
type: Person

Anton Korobeynikov
type: Person

Ekaterina Sakharova
type: Person

Guy Cochrane
type: Person

Josephine Burgin
type: Person

Lorna J Richardson
type: Person

Martin Beracochea
type: Person

Maxim Scheremetjew
type: Person

Michael R Crusoe
type: Person

Miguel Boland
type: Person

Olga Kunyavskaya
type: Person

Robert D Finn
type: Person

Simon C Potter
type: Person

Varsha Kale
type: Person

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

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5s_pattern
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CGC_config
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CGC_postfixes
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EggNOG_db
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EggNOG_diamond_db
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HMM_name_database
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HMM_omit_alignment
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forward_reads
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ips_header
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no_tax_flag_file
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qc-status
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qc_summary
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rna-count
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stats
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taxonomy-summary_folder
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MGnify - raw-reads analysis pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/27
creator: https://workflowhub.eu/people/354
creator: https://workflowhub.eu/people/355
creator: Alex L Mitchell
creator: Alex Shlemov
creator: Alexandre Almeida
creator: Alla Lapidus
creator: Anton Korobeynikov
creator: Ekaterina Sakharova
creator: Guy Cochrane
creator: Josephine Burgin
creator: Lorna J Richardson
creator: Martin Beracochea
creator: Maxim Scheremetjew
creator: Michael R Crusoe
creator: Miguel Boland
creator: Olga Kunyavskaya
creator: Robert D Finn
creator: Simon C Potter
creator: Varsha Kale
date created: 2022-06-07T08:40:40Z
date modified: 2023-01-16T14:01:12Z
description: MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations. Documentation: https://docs.mgnify.org/en/latest/analysis.html#raw-reads-analysis-pipeline
input: 5.8s_pattern
input: 5s_pattern
input: CGC_config
input: CGC_postfixes
input: EggNOG_data_dir
input: EggNOG_db
input: EggNOG_diamond_db
input: HMM_gathering_bit_score
input: HMM_name_database
input: HMM_omit_alignment
input: InterProScan_applications
input: InterProScan_databases
input: InterProScan_outputFormat
input: cgc_chunk_size
input: forward_reads
input: func_ann_names_hmmer
input: func_ann_names_ips
input: go_config
input: hmmsearch_header
input: ips_header
input: ko_file
input: lsu_db
input: lsu_label
input: lsu_otus
input: lsu_tax
input: other_ncRNA_models
input: protein_chunk_size_IPS
input: protein_chunk_size_hmm
input: qc_min_length
input: reverse_reads
input: rfam_model_clans
input: rfam_models
input: single_reads
input: ssu_db
input: ssu_label
input: ssu_otus
input: ssu_tax
keywords: Workflows, CWL, Metagenomics
license: Apache-2.0
output: chunking_nucleotides
output: chunking_proteins
output: completed_flag_file
output: compressed_files
output: fastp_filtering_json_report
output: functional_annotation_folder
output: hashsum_paired
output: hashsum_single
output: motus_output
output: no_cds_flag_file
output: no_tax_flag_file
output: qc-statistics
output: qc-status
output: qc_summary

Galaxy
type: ComputerLanguage

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Case 5 Normalized VCFs
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Case 5 Normalized VCFs (Removed <NON_REF>)
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_anonymous_output_1
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Trio Analysis
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Jasper Ouwerkerk
date created: 2023-03-01T15:35:32Z
date modified: 2023-09-05T08:11:49Z
description: To discover causal mutations of inherited diseases it’s common practice to do a trio analysis. In a trio analysis DNA is sequenced of both the patient and parents. Using this method, it’s possible to identify multiple inheritance patterns. Some examples of these patterns are autosomal recessive, autosomal dominant, and de-novo variants, which are represented in the figure below. To elaborate, the most left tree shows an autosomal dominant inhertitance pattern where the offspring inherits a faulty copy of the gene from one of the parents. To discover these mutations either whole exome sequencing (WES) or whole genome sequencing (WGS) can be used. With these technologies it is possible to uncover the DNA of the parents and offspring to find (shared) mutations in the DNA. These mutations can include insertions/deletions (indels), loss of heterozygosity (LOH), single nucleotide variants (SNVs), copy number variations (CNVs), and fusion genes. In this workflow we will also make use of the HTSGET protocol, which is a program to download our data securely and savely. This protocol has been implemented in the EGA Download Client Tool: toolshed.g2.bx.psu.edu/repos/iuc/ega_download_client/pyega3/4.0.0+galaxy0 tool, so we don’t have to leave Galaxy to retrieve our data. We will not start our analysis from scratch, since the main goal of this tutorial is to use the HTSGET protocol to download variant information from an online archive and to find the causative variant from those variants. If you want to learn how to do the analysis from scratch, using the raw reads, you can have a look at the Exome sequencing data analysis for diagnosing a genetic disease tutorial.
input: pedigree
is based on: https://workflowhub.eu/workflows/363
keywords: variant-analysis
license: CC-BY-4.0
output: Case 5 GEMINI results
output: Case 5 Merged VCF
output: Case 5 Normalized VCFs
output: Case 5 Normalized VCFs (Removed <NON_REF>)
output: Case 5 SnpEff Annotated vcf
output: Case 5 VCFs
output: Case 5 VCFs (Fixed Header and Chr)
output: Case 5 VCFs.gz
output: Case 5 gene.iobio results
output: EGA Download Client: authorized datasets
output: GEMINI Database
output: List of Case 5 VCFs
output: List of files in EGAD00001008392
output: SnpEff Annotated vcf_bgzip
output: _anonymous_output_1
producer: https://workflowhub.eu/projects/12
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 2

Galaxy
type: ComputerLanguage

inputdata_cesm_2_1_3_B1850_f19_g17_tar
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user_nl_cam_rs
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Workflow for running the Community Earth System Model in fully coupled mode
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2022-06-12T19:01:32Z
date modified: 2023-01-16T14:01:23Z
description: This workflow demonstrates the usage of the [Community Earth System Model](https://www.cesm.ucar.edu/) on Galaxy Europe. A fully coupled B1850 compset with resolution f19_g17 is run for 1 month. ![](https://nordicesmhub.github.io/GEO4962/fig/newcase.png)
input: inputdata_cesm_2_1_3_B1850_f19_g17_tar
input: user_nl_cam_rs
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/18
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

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VGP-meryldb-creation-trio/main
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2022-06-14T02:01:13Z
date modified: 2025-12-12T02:01:33Z
description: # VGP Workflow #1 This workflow collects the metrics on the properties of the genome under consideration by analyzing the k-mer frequencies. It provides information about the genomic complexity, such as the genome size and levels of heterozygosity and repeat content, as well about the data quality. It uses reads from two parental genomes to partition long reads from the offspring into haplotype-specific k-mer databases. ### Inputs - Collection of Hifi long reads in FASTQ format - Paternal short-read Illumina sequencing reads in FASTQ format - Maternal short-read Illumina sequencing reads in FASTQ format ### Outputs - Meryl databases of k-mer counts - Child - Paternal haplotype - Maternal haplotype - GenomeScope metrics of child and parental genomes - Linear plot - Log plot - Transformed linear plot - Transformed log plot - Summary - Model - Model parameteres
input: #main/Collection of Paired Reads - Maternal
input: #main/Collection of Paired Reads - Paternal
input: #main/K-mer length
input: #main/Pacbio Hifi reads
input: #main/Ploidy
is part of: https://workflowhub.eu/collections/8
license: CC-BY-4.0
output: #main/GenomeScope linear plot (child)
output: #main/GenomeScope linear plot (maternal)
output: #main/GenomeScope linear plot (paternal)
output: #main/GenomeScope log plot (child)
output: #main/GenomeScope log plot (maternal)
output: #main/GenomeScope log plot (paternal)
output: #main/GenomeScope model (child)
output: #main/GenomeScope summary (child)
output: #main/GenomeScope transformed linear plot (child)
output: #main/GenomeScope transformed linear plot (maternal)
output: #main/GenomeScope transformed linear plot (paternal)
output: #main/GenomeScope transformed log plot (child)
output: #main/GenomeScope transformed log plot (maternal)
output: #main/GenomeScope transformed log plot (paternal)
output: #main/Meryl mat.meryldb
output: #main/Meryl pat.meryldb
output: #main/Meryl read-db.meryldb
output: #main/_anonymous_output_1
output: #main/_anonymous_output_2
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
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VGP-meryldb-creation/main
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2022-06-14T02:01:14Z
date modified: 2026-01-25T02:01:20Z
description: # VGP Workflow #1 This workflow produces a Meryl database and Genomescope outputs that will be used to determine parameters for following workflows, and assess the quality of genome assemblies. Specifically, it provides information about the genomic complexity, such as the genome size and levels of heterozygosity and repeat content, as well about the data quality. ### Inputs - Collection of Hifi long reads in FASTQ format ### Outputs - Meryl Database of kmer counts - GenomeScope - Linear plot - Log plot - Transformed linear plot - Transformed log plot - Summary - Model - Model parameteres
input: #main/Collection of Pacbio Data
input: #main/K-mer length
input: #main/Ploidy
license: CC-BY-4.0
output: #main/GenomeScope linear plot
output: #main/GenomeScope log plot
output: #main/GenomeScope model
output: #main/GenomeScope model parameters
output: #main/GenomeScope summary
output: #main/GenomeScope transformed linear plot
output: #main/GenomeScope transformed log plot
output: #main/Merged Meryl Database
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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(Hybrid) Metagenomics workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/46
creator: https://workflowhub.eu/people/653
creator: https://workflowhub.eu/people/84
date created: 2025-09-09T12:28:46Z
date modified: 2026-01-23T10:25:06Z
description: **Workflow (hybrid) metagenomic assembly and binning** - Workflow Illumina Quality: - Sequali (control) - hostile contamination filter - fastp (quality trimming) - Workflow Longread Quality: - NanoPlot (control) - fastplong (quality trimming) - hostile contamination filter - Kraken2 taxonomic classification of FASTQ reads - SPAdes/Flye (Assembly) - Medaka/PyPolCA (Assembly polishing) - QUAST (Assembly quality report) (optional) - Workflow binnning - Metabat2/MaxBin2/SemiBin - Binette - BUSCO - GTDB-Tk (optional) - Workflow Genome-scale metabolic models https://workflowhub.eu/workflows/372 - CarveMe (GEM generation) - MEMOTE (GEM test suite) - SMETANA (Species METabolic interaction ANAlysis) Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default **All tool CWL files and other workflows can be found here:** https://gitlab.com/m-unlock/cwl/ **How to setup and use an UNLOCK workflow:** https://docs.m-unlock.nl/docs/workflows/setup.html
input: Annotate bins
input: Annotate unbinned
input: Assembly choice
input: Bakta DB
input: Run binning workflow
input: Bracken levels
input: BUSCO dataset
input: Deduplicate illumina reads
input: Output Destination
input: eggnog_dbs
input: Fastq rich (ONT)
input: Deterministic Flye
input: Genome Size
input: gtdbtk data directory
input: Identifier
input: Forward reads
input: Filter human reads
input: Read length
input: Illumina reference filter db
input: Reverse reads
input: InterProScan applications
input: InterProScan 5 directory
input: Keep filtered reads
input: SAPP kofamscan limit
input: Kraken2 confidence threshold
input: Kraken2 database
input: Kraken2 standard report
input: Adapter fasta
input: Disable adapter trimming
input: End adapter
input: Filter human illumina reads
input: Maximum length limit
input: Mean quality
input: Minimum length required
input: Poly_x_min_len
input: Qualified_quality_phred
input: Longread reference filter db
input: start_adapter
input: Trim_front
input: Trim_poly_x
input: trim_tail
input: Memory usage (MB)
input: When working with metagenomes
input: Oxford Nanopore reads
input: Only spades assembler
input: ONT Basecalling model used for MEDAKA
input: Output BAM file
input: PacBio reads
input: Run eggNOG-mapper
input: Use Flye
input: Run InterProScan
input: Run kofamscan
input: Run kraken2 on Illumina reads
input: Run Maxbin2
input: Use Medaka
input: Use PyPolCA
input: Run SemiBin
input: Use SPAdes
input: SemiBin Environment
input: Skip bakta CRISPR
input: Run Bracken
input: Input URLs used for this run
input: Number of threads
input: Keep mapped reads
input: Use SPAdes scaffolds
is based on: https://workflowhub.eu/workflows/367
keywords: Metagenomics, Assembly, illumina, binning
license: Apache-2.0
output: Assembly output
output: Binning output

COVID-19: VARSCAN
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-06-18T22:57:27Z
date modified: 2023-02-13T14:06:46Z
description: Detects SNPs and INDELs using VARSCAN2.
input: List of Illumina accessions
input: Reference genome
input: Reference genome annotation
keywords: Galaxy, VARSCAN2, SNPs, INDELs
license: Apache-2.0
output: _anonymous_output_10
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producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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Metagenomic GEMs from Assembly
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/46
creator: https://workflowhub.eu/people/84
date created: 2022-07-07T08:23:15Z
date modified: 2023-01-16T14:01:55Z
description: ### Workflow for Metagenomics from bins to metabolic models (GEMs) **Summary** - Prodigal gene prediction - CarveMe genome scale metabolic model reconstruction - MEMOTE for metabolic model testing - SMETANA Species METabolic interaction ANAlysis Other UNLOCK workflows on WorkflowHub: https://workflowhub.eu/projects/16/workflows?view=default **All tool CWL files and other workflows can be found here:** Tools: https://gitlab.com/m-unlock/cwl Workflows: https://gitlab.com/m-unlock/cwl/workflows **How to setup and use an UNLOCK workflow:** https://m-unlock.gitlab.io/docs/setup/setup.html
input: Genome/bin
input: Output Destination (prov only)
input: Identifier used
input: solver
input: number of threads
keywords: Metagenomics, Genomics, GEM, carveme, memote
license: Apache-2.0
output: CarveMe GEMs folder
output: GEMstats
output: MEMOTE outputs folder
output: Protein files folder
output: SMETANA output
producer: https://workflowhub.eu/projects/16
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

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De novo digitisation
type: ComputationalWorkflow
type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/159
creator: https://workflowhub.eu/people/219
creator: Paul Brack
date created: 2022-12-14T16:05:01Z
date modified: 2022-12-14T16:06:43Z
description: An example workflow to allow users to run the Specimen Data Refinery tools on data provided in an input CSV file.
input: DLA Region - Training Model
input: DLA Text - Training Model
is based on: https://workflowhub.eu/workflows/373
keywords: Default-SDR
license: CC-BY-4.0
output: html_file
output: list_output_txt
output: out_file1
output: output
producer: https://workflowhub.eu/projects/72
programming language: Galaxy
sd publisher: about.workflowhub.eu
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DLA-Collections-test
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type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/159
creator: https://workflowhub.eu/people/219
date created: 2022-07-08T13:04:19Z
date modified: 2023-01-16T14:01:59Z
description: An example workflow for the Specimen Data Refinery tool, allowing an individual tool to be used
keywords: Default-SDR, multi-specimen-input, collections, validated-2022-06-29
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/72
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Oliver Woolland
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Galaxy
type: ComputerLanguage

HTR-Collections-test
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/159
creator: https://workflowhub.eu/people/219
creator: Oliver Woolland
date created: 2022-07-08T13:05:11Z
date modified: 2023-01-16T14:02:00Z
description: An example workflow for the Specimen Data Refinery tool, allowing an individual tool to be used
keywords: Default-SDR, multi-specimen-input, collections, validated-2022-06-29
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/72
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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ABR Threshold Detection
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2022-07-18T08:08:04Z
date modified: 2023-01-16T14:02:01Z
description: # ABR\_Threshold_Detection ## What is this? This code can be used to automatically determine hearing thresholds from ABR hearing curves. One of the following methods can be used for this purpose: + neural network (NN) training, + calibration of a self-supervised sound level regression (SLR) method on given data sets with manually determined hearing thresholds. ## Installation: Run inside the [src](./src) directory: ### Installation as python package ``` pip install -e ./src (Installation as python package) ``` ### Installation as conda virtual environment ``` conda create -n abr_threshold_detection python=3.7 conda activate abr_threshold_detection conda install pip pip install -e ./src ``` ## Usage: Data files can be downloaded here: [https://zenodo.org/deposit/5779876](https://zenodo.org/deposit/5779876). For the Jupyter Notebooks (see the [`notebooks`](./notebooks) directory) to run, the path to the data has to be defined. For this, see the corresponding documentation of the respective notebooks. ### Using NNs (`./src/ABR_ThresholdFinder_NN`) The neural network models were trained in `./src/notebooks/GMCtrained_NN*_training.ipynb` with GMC data and in `./src/notebooks/INGtrained_NN*_training.ipynb` with ING data. ``` import ABR_ThresholdFinder_NN.data_preparation as dataprep from ABR_ThresholdFinder_NN.models import create_model_1, compile_model_1 ``` For automatic threshold detection based on NNs, `GMCtrained_NN_threshold_detection.ipynb` and `INGtrained_NN_threshold_detection.ipynb` in `./src/notebooks` can be used. ``` import ABR_ThresholdFinder_NN.data_preparation as dataprep import ABR_ThresholdFinder_NN.thresholder as abrthr ``` ### Using the SLR method (`./src/ABR_ThresholdFinder_SLR`) In `./src/notebooks/GMCcalibrated_SLR_threshold_detection.ipynb` and `./src/notebooks/INGcalibrated_SLR_threshold_detection.ipynb` it is shown how to use the module to: + train a threshold detector on a data set and estimate the thresholds + save a trained model + load a mode…
keywords: Machine Learning
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/67
programming language: Python
sd publisher: about.workflowhub.eu
version: 1

COMPSs
type: ComputerLanguage

Lysozyme in Water COMPSs workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0001-6401-6229
creator: Justin Lemkul
date created: 2022-07-20T10:53:54Z
date modified: 2023-11-24T08:56:09Z
description: Lysozyme in Water simplest version, from COMPSs Tutorial. The original idea of this worklfow comes from http://www.mdtutorials.com/gmx/lysozyme/index.html
keywords: GROMACS, PyCOMPSs, Marenostrum IV, Supercomputer, non_data_persistence
license: Apache-2.0
producer: https://workflowhub.eu/projects/102
producer: https://workflowhub.eu/projects/172
programming language: COMPSs
sd publisher: about.workflowhub.eu
version: 1

Assembly using Tophat2 and annotation (alternate)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-06-18T23:03:09Z
date modified: 2023-02-13T14:06:46Z
description: Alignment, assembly and annotation of RNQSEQ reads using TOPHAT (without filtering out host reads).
input: List of Illumina accessions
input: Reference annotation
input: Reference genome.
input: SARS-CoV-2 proteins
keywords: Galaxy, Tophat2, Assembly, Alignment, RNASEQ, covid-19
license: Apache-2.0
output: _anonymous_output_10
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output: fasta
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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Air Quality Prediction Prototype
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/159
creator: https://workflowhub.eu/people/37
date created: 2022-07-29T11:16:20Z
date modified: 2023-01-16T14:02:21Z
description: A prototype implementation of the Air Quality Prediction pipeline in Galaxy, using CWL tools.
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license: CC-BY-4.0
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output: _anonymous_output_10
output: _anonymous_output_11
output: _anonymous_output_12
output: _anonymous_output_13
output: _anonymous_output_2
output: _anonymous_output_4
output: _anonymous_output_5
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output: _anonymous_output_7
output: _anonymous_output_8
output: _anonymous_output_9
producer: https://workflowhub.eu/projects/103
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

dna-seq-varlociraptor workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Johannes Köster
date created: 2022-09-02T10:48:32Z
date modified: 2023-01-16T14:02:24Z
description: # Snakemake workflow: dna-seq-varlociraptor [![Snakemake](https://img.shields.io/badge/snakemake-≥6.3.0-brightgreen.svg)](https://snakemake.github.io) [![GitHub actions status](https://github.com/snakemake-workflows/dna-seq-varlociraptor/workflows/Tests/badge.svg?branch=master)](https://github.com/snakemake-workflows/dna-seq-varlociraptor/actions?query=branch%3Amaster+workflow%3ATests) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4675661.svg)](https://doi.org/10.5281/zenodo.4675661) A Snakemake workflow for calling small and structural variants under any kind of scenario (tumor/normal, tumor/normal/relapse, germline, pedigree, populations) via the unified statistical model of [Varlociraptor](https://varlociraptor.github.io). ## Usage The usage of this workflow is described in the [Snakemake Workflow Catalog](https://snakemake.github.io/snakemake-workflow-catalog/?usage=snakemake-workflows%2Fdna-seq-varlociraptor). If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) repository and its DOI (see above).
license: MIT
producer: https://workflowhub.eu/projects/55
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Adrián Muñoz-Civico
type: Person

Daniel López-López
type: Person

Nextflow
type: ComputerLanguage

CNV_pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/4
creator: Adrián Muñoz-Civico
creator: Daniel López-López
date created: 2022-10-11T10:58:35Z
date modified: 2023-01-16T14:02:25Z
description: # StructuralVariants Workflow
is based on: https://workflowhub.eu/workflows/383
keywords: CODEX2, TransBioNet, ExomeDepth, variant calling, cancer, manta, GRIDS, structural variants
license: Apache-2.0
producer: https://workflowhub.eu/projects/70
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 3

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

A workflow for marine Genomic Observatories data analysis
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Haris Zafeiropoulos
creator: https://workflowhub.eu/people/27
date created: 2023-05-16T20:41:04Z
date modified: 2023-05-16T22:01:12Z
description: # metaGOflow: A workflow for marine Genomic Observatories' data analysis ![logo](https://raw.githubusercontent.com/hariszaf/metaGOflow-use-case/gh-pages/assets/img/metaGOflow_logo_italics.png) ## An EOSC-Life project The workflows developed in the framework of this project are based on `pipeline-v5` of the MGnify resource. > This branch is a child of the [`pipeline_5.1`](https://github.com/hariszaf/pipeline-v5/tree/pipeline_5.1) branch > that contains all CWL descriptions of the MGnify pipeline version 5.1. ## Dependencies To run metaGOflow you need to make sure you have the following set on your computing environmnet first: - python3 [v 3.8+] - [Docker](https://www.docker.com) [v 19.+] or [Singularity](https://apptainer.org) [v 3.7.+]/[Apptainer](https://apptainer.org) [v 1.+] - [cwltool](https://github.com/common-workflow-language/cwltool) [v 3.+] - [rdflib](https://rdflib.readthedocs.io/en/stable/) [v 6.+] - [rdflib-jsonld](https://pypi.org/project/rdflib-jsonld/) [v 0.6.2] - [ro-crate-py](https://github.com/ResearchObject/ro-crate-py) [v 0.7.0] - [pyyaml](https://pypi.org/project/PyYAML/) [v 6.0] - [Node.js](https://nodejs.org/) [v 10.24.0+] - Available storage ~235GB for databases ### Storage while running Depending on the analysis you are about to run, disk requirements vary. Indicatively, you may have a look at the metaGOflow publication for computing resources used in various cases. ## Installation ### Get the EOSC-Life marine GOs workflow ```bash git clone https://github.com/emo-bon/MetaGOflow cd MetaGOflow ``` ### Download necessary databases (~235GB) You can download databases for the EOSC-Life GOs workflow by running the `download_dbs.sh` script under the `Installation` folder. ```bash bash Installation/download_dbs.sh -f [Output Directory e.g. ref-dbs] ``` If you have one or more already in your system, then create a symbolic link pointing at the `ref-dbs` folder or at one of its subfolders/files. The final structure of the DB directory should be like t…
is based on: https://workflowhub.eu/workflows/384
is part of: https://workflowhub.eu/collections/33
keywords: Biodiversity
license: Apache-2.0
producer: emo-bon
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 3

COMPSs
type: ComputerLanguage

BackTrackBB: Multi-band array detection and location of seismic sources
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0001-6401-6229
creator: Claudio Satriano
creator: Natalia Poiata
date created: 2023-01-23T11:16:11Z
date modified: 2024-03-07T09:01:21Z
description: BackTrackBB is a program for detection and space-time location of seismic sources based on multi-scale, frequency-selective statistical coherence of the wave field recorded by dense large-scale seismic networks and local antennas. The method is designed to enhance coherence of the signal statistical features across the array of sensors and consists of three steps. They are signal processing, space-time imaging and detection and location. Source with inputs and outputs included (too big for WorkflowHub): [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7788030.svg)](https://doi.org/10.5281/zenodo.7788030) More information: https://backtrackbb.github.io/
is based on: https://workflowhub.eu/workflows/386
keywords: PyCOMPSs, Seismic Detection, BackTrackBB, Earthquake Detection, Marenostrum IV, Supercomputer, non_data_persistence
license: CECILL-2.1
producer: https://workflowhub.eu/projects/102
producer: https://workflowhub.eu/projects/172
programming language: COMPSs
sd publisher: about.workflowhub.eu
version: 2

KNIME
type: ComputerLanguage

DRC_template_toxicity
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/319
date created: 2022-09-26T09:15:13Z
date modified: 2023-01-16T14:02:31Z
description: Generates Dose-response curve fits on cell-based toxicity data. Outliers of replicate data-sets can be removed by setting a threshold for standard deviation (here set to 25). Curve fits for compounds showing low response can be removed by setting a threshold for minimum activity (here set to 75% confluence). This workflow needs R-Server to run in the back-end. Start R and run the following command: library(Rserve); Rserve(args = "--vanilla")
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/94
programming language: KNIME
sd publisher: about.workflowhub.eu
version: 1

KNIME
type: ComputerLanguage

DRC_cellbased_OutlierDetection
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/319
date created: 2022-09-26T09:58:39Z
date modified: 2023-01-16T14:02:32Z
description: This workflow can be used to fit dose-response curves from normalised cell-based assay data (%confluence) using the KNIME HCS extension. The workflow expects triplicates for each of eight test concentrations. This workflow needs R-Server to run in the back-end. Start R and run the following command: library(Rserve); Rserve(args = "--vanilla"). Three types of outliers can be removed: 1 - Outliers from triplicate measurement (standard deviation cut-off can be selected), 2 - inactive and weekly active compounds (% confluence cut-offs can be selected), 3 - toxic concentrations (cut-off for reduction in confluence with stepwise increasing concentration can be selected) Output are two dose-response curve fits per compound for pre and post outlier removal with graphical representation and numerical fit parameters.
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/94
programming language: KNIME
sd publisher: about.workflowhub.eu
version: 1

KNIME
type: ComputerLanguage

DRC_biochemical_toECBD
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/319
date created: 2022-09-26T10:15:37Z
date modified: 2023-01-16T14:02:33Z
description: This workflow can be used to fit dose-response curves from normalised biochemical assay data (%Inhibition) using the HCS extension. This workflow needs R-Server to run in the back-end. Start R and run the following command: library(Rserve); Rserve(args = "--vanilla") IC50 values will not be extrapolated outside the tested concentration range For activity classification the following criteria are applied: - maximum (average % inhibion) >25 % and slope is >0 and IC50 > 5 µM or - minimum (average % inhibion) >75 % Results are formatted for upload to the European Chemical Biology Database (ECBD)
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/94
programming language: KNIME
sd publisher: about.workflowhub.eu
version: 1

Unicycler assembly and annotation
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-06-18T23:07:23Z
date modified: 2023-02-13T14:06:46Z
description: Alignment, assembly RNASEQ reads and annotation of generated transcripts.
input: List of Illumina accessions
input: SARS-CoV-2 proteins
keywords: Unicycler, Alignment, Assembly, Annotation, RNASEQ, covid-19
license: Apache-2.0
output: unicycler assembly and annotation-outputs-
output: _anonymous_output_10
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output: _anonymous_output_5
output: _anonymous_output_6
output: _anonymous_output_7
output: _anonymous_output_8
output: _anonymous_output_9
output: fast
output: fastq
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

List of Illumina accessions
type: FormalParameter
conforms to: 1.0-RELEASE

Reference genome
type: FormalParameter
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Reference genome annotation
type: FormalParameter
conforms to: 1.0-RELEASE

SARS-CoV-2 proteins
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_10
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fasta
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Alignment score from Kpax to analyse structures
type: FormalParameter
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CATH family ids
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Obsolete and inconsistent CATH domain StIs
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Filename for residue-mapped CATH domain StIs
type: FormalParameter
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Core domain structure (.pdb)
type: FormalParameter
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Database to select to compute core average structure
type: FormalParameter
conforms to: 1.0-RELEASE

To store all the domain-like StIs
type: FormalParameter
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To store all failed domain StIs
type: FormalParameter
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Filename to store family ids per iteration
type: FormalParameter
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Iteration number starting from 0
type: FormalParameter
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Threshold for minimum domain length
type: FormalParameter
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Parameter file for current iteration
type: FormalParameter
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The directory for storing all PDB files
type: FormalParameter
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Pfam family ids
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Obsolete and inconsistent Pfam domain StIs
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Filename for residue-mapped Pfam domain StIs
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CATH cross-mapped domain StIs from previous iteration
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Pfam cross-mapped domain StIs from previous iteration
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Score threshold for given alignment score from Kpax
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Directory for storing all SIFTS files
type: FormalParameter
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To store all the true domain StIs
type: FormalParameter
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Filename to store unmapped and not properly aligned instances from CATH
type: FormalParameter
conforms to: 1.0-RELEASE

Filename to store unmapped but structurally well aligned instances from CATH
type: FormalParameter
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Filename to store unmapped and not properly aligned instances from Pfam
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Filename to store unmapped but structurally well aligned instances from Pfam
type: FormalParameter
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Filename with alignment scores for unmapped instances
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Alignment results for CATH unmapped instances
type: FormalParameter
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Alignment results for Pfam unmapped instances
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conforms to: 1.0-RELEASE

Domain-like StIs
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Failed domain StIs
type: FormalParameter
conforms to: 1.0-RELEASE

All CATH cross-mapped domin StIs family-wise together
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All Pfam domain StIs cross-mapped to CATH family-wise
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conforms to: 1.0-RELEASE

Alignment results from Kpax for all cross-mapped families
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Average structures per cross-mapped Pfam family for CATH StIs at family level
type: FormalParameter
conforms to: 1.0-RELEASE

Core domain StIs
type: FormalParameter
conforms to: 1.0-RELEASE

Core domain structure (.pdb)
type: FormalParameter
conforms to: 1.0-RELEASE

CATH domain StIs cross-mapped to Pfam family-wise
type: FormalParameter
conforms to: 1.0-RELEASE

Pfam domin StIs cross-mapped to CATH family-wise
type: FormalParameter
conforms to: 1.0-RELEASE

Cross-mapped families with CATH domain StIs passing the threshold
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Cross-mapped families with Pfam domain StIs passing the threshold
type: FormalParameter
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Merged cross-mapped and residue-mapped domain StIs from CATH
type: FormalParameter
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Merged cross-mapped and residue-mapped domain StIs from Pfam
type: FormalParameter
conforms to: 1.0-RELEASE

Family ids per iteration
type: FormalParameter
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Parameter file for next iteration of the workflow
type: FormalParameter
conforms to: 1.0-RELEASE

Average structures per cross-mapped CATH family for Pfam StIs at family level
type: FormalParameter
conforms to: 1.0-RELEASE

Obsolete and inconsistent domain StIs from CATH
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Obsolete and inconsistent domain StIs from Pfam
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All CATH residue-mapped domain StIs with domain labels
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conforms to: 1.0-RELEASE

All Pfam residue-mapped domain StIs with domain labels
type: FormalParameter
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True domain StIs per iteration
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All un-mapped domin StIs from CATH
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Failed domain StIs from CATH
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All Pfam un-mapped domin StIs
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Failed domain StIs from Pfam
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Domain-like StIs from Pfam
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Common Workflow Language
type: ComputerLanguage
alternate name: CWL

CroMaSt: A workflow for assessing protein domain classification by cross-mapping of structural instances between domain databases and structural alignment
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Isaure Chauvot de Beauchêne
creator: Hrishikesh Dhondge
creator: Marie-Dominique Devignes
date created: 2023-06-20T12:06:49Z
date modified: 2023-06-20T12:08:47Z
description: # CroMaSt: A workflow for assessing protein domain classification by cross-mapping of structural instances between domain databases and structural alignment CroMaSt (**Cro**ss **Ma**pper of domain **St**ructural instances) is an automated iterative workflow to clarify the assignment of protein domains to a given domain type of interest, based on their 3D structure and by cross-mapping of domain structural instances between domain databases. CroMaSt (for Cross-Mapper of domain Structural instances) will classify all structural instances of a given domain type into 4 different categories (**Core**, **True**, **Domain-like**, and **Failed**). ## Requirements 1. [Conda](https://docs.conda.io/projects/conda/en/latest/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html) 2. [Kpax](http://kpax.loria.fr/download.php) Download and install conda (or Miniconda) and Kpax by following the instructions from their official site. ## Get it running (Considering the requirements are already met) 1. Clone the repository and change the directory ``` git clone https://gitlab.inria.fr/capsid.public_codes/CroMaSt.git cd CroMaSt ``` 2. Create the conda environment for the workflow ``` conda env create --file yml/environment.yml conda activate CroMaSt ``` 3. Change the path of variables in paramter file ``` sed -i 's/\/home\/hdhondge\/CroMaSt\//\/YOUR\/PATH\/TO_CroMaSt\//g' yml/CroMaSt_input.yml ``` 4. Create the directory to store files from PDB and SIFTS (if not already) ``` mkdir PDB_files SIFTS ``` 5. Download the source input data ``` cwl-runner Tools/download_data.cwl yml/download_data.yml ``` ## Basic example ### 1. First, we will run the workflow for the KH domain with family identifiers `RRM_1` and `RRM` in Pfam and CATH, respectively. Run the workflow - ``` cwl-runner --parallel --outdir=Results/ CroMaSt.cwl yml/CroMaSt_input.yml ``` ### 2. Once the iteration is complete, check the `new_param.yml` file from the `outputdir` (Results), if there is any family identifier in…
input: Alignment score from Kpax to analyse structures
input: CATH family ids
input: Obsolete and inconsistent CATH domain StIs
input: Filename for residue-mapped CATH domain StIs
input: Core domain structure (.pdb)
input: Database to select to compute core average structure
input: To store all the domain-like StIs
input: To store all failed domain StIs
input: Filename to store family ids per iteration
input: Iteration number starting from 0
input: Threshold for minimum domain length
input: Parameter file for current iteration
input: The directory for storing all PDB files
input: Pfam family ids
input: Obsolete and inconsistent Pfam domain StIs
input: Filename for residue-mapped Pfam domain StIs
input: CATH cross-mapped domain StIs from previous iteration
input: Pfam cross-mapped domain StIs from previous iteration
input: Score threshold for given alignment score from Kpax
input: Directory for storing all SIFTS files
input: To store all the true domain StIs
input: Filename to store unmapped and not properly aligned instances from CATH
input: Filename to store unmapped but structurally well aligned instances from CATH
input: Filename to store unmapped and not properly aligned instances from Pfam
input: Filename to store unmapped but structurally well aligned instances from Pfam
input: Filename with alignment scores for unmapped instances
is based on: https://workflowhub.eu/workflows/390
keywords: Pfam, CATH, Protein domains, data integration
license: MIT
output: Alignment results for CATH unmapped instances
output: Alignment results for Pfam unmapped instances
output: Domain-like StIs
output: Failed domain StIs
output: All CATH cross-mapped domin StIs family-wise together
output: All Pfam domain StIs cross-mapped to CATH family-wise
output: Alignment results from Kpax for all cross-mapped families
output: Average structures per cross-mapped Pfam family for CATH StIs at family level
output: Core domain StIs
output: Core domain structure (.pdb)
output: CATH domain StIs cross-mapped to Pfam family-wise
output: Pfam domin StIs cross-mapped to CATH family-wise
output: Cross-mapped families with CATH domain StIs passing the threshold
output: Cross-mapped families with Pfam domain StIs passing the threshold
output: Merged cross-mapped and residue-mapped domain StIs from CATH
output: Merged cross-mapped and residue-mapped domain StIs from Pfam
output: Family ids per iteration
output: Parameter file for next iteration of the workflow
output: Average structures per cross-mapped CATH family for Pfam StIs at family level
output: Obsolete and inconsistent domain StIs from CATH
output: Obsolete and inconsistent domain StIs from Pfam
output: All CATH residue-mapped domain StIs with domain labels
output: All Pfam residue-mapped domain StIs with domain labels
output: True domain StIs per iteration
output: All un-mapped domin StIs from CATH
output: Failed domain StIs from CATH
output: Domain-like StIs from CATH
output: All Pfam un-mapped domin StIs
output: Failed domain StIs from Pfam
output: Domain-like StIs from Pfam
producer: CAPSID
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 2

Nextflow
type: ComputerLanguage

IndexReferenceFasta-nf
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/123
date created: 2022-10-12T02:34:32Z
date modified: 2023-01-16T14:02:39Z
description: # IndexReferenceFasta-nf =========== - [Description](#description) - [Diagram](#diagram) - [User guide](#user-guide) - [Benchmarking](#benchmarking) - [Workflow summaries](#workflow-summaries) - [Metadata](#metadata) - [Component tools](#component-tools) - [Required (minimum) inputs/parameters](#required-minimum-inputsparameters) - [Additional notes](#additional-notes) - [Help/FAQ/Troubleshooting](#helpfaqtroubleshooting) - [Acknowledgements/citations/credits](#acknowledgementscitationscredits) --- ## Description This is a flexible pipeline for generating common reference genome index files for WGS data analysis. IndexReferenceFasta-nf is a Nextflow (DSL2) pipeline that runs the following tools using Singularity containers: * Samtools faidx * BWA index * GATK CreateSequenceDictionary ## Diagram ## User guide **1. Set up** Clone this repository by running: ``` git clone https://github.com/Sydney-Informatics-Hub/IndexReferenceFasta-nf.git cd IndexReferenceFasta-nf ``` **2. Generate indexes** Users can specify which index files to create by using the `--samtools`, `--bwa`, and/or `--gatk` flags. All are optional. Run the pipeline with: ``` nextflow run main.nf /path/to/ref.fasta --bwa --samtools --gatk ``` ## Benchmarking ### Human hg38 reference assembly @ Pawsey's Nimbus (NCPU/task = 1) |task_id|hash |native_id|name |status |exit|submit |duration |realtime |%cpu |peak_rss|peak_vmem|rchar |wchar | |-------|---------|---------|--------------|---------|----|-------|----------|----------|-------|--------|---------|-------|-------| |3 |27/33fffc|131621 |samtools_index|COMPLETED|0 |55:44.9|12.2s |12s |99.20% |6.3 MB |11.8 MB |3 GB |19.1 KB| |1 |80/f03e46|131999 |gatk_index |COMPLETED|0 |55:46.7|22.6s |22.3s |231.90%|3.8 GB |37.1 GB |3.1 GB |726 KB | |2 |ea/e29535|131594 |bwa_index |COMPLETED|0 |55:44.9|1h 50m 16s|1h 50m 15s|99.50% |4.5 GB |4.5 GB |12.1 GB|8.2 GB | ## Workflow summaries ### Metadata |metadata field | workflow_name / workflow_version | |-------------------|:…
keywords: Bioinformatics, Nextflow, WGS, index, referencegenome, SAMTools, GATK, BWA, Genomics
license: LGPL-3.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/43
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

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date created: 2026-04-08T02:01:49Z
date modified: 2026-04-08T02:01:49Z
description: Complete CUT&RUN/CUT&TAG analysis workflow for paired-end sequencing data. Processes raw FASTQ files through adapter removal (cutadapt) and alignment (Bowtie2 with dovetail option enabled). Applies quality filtering (MAPQ ≥ 30, concordant pairs only), converts BAM to BED format, and performs peak calling using MACS2 with parameters optimized for the punctate signal profile characteristic of CUT&RUN/CUT&TAG experiments.
input: #main/PE fastq input
input: #main/adapter_forward
input: #main/adapter_reverse
input: #main/effective_genome_size
input: #main/normalize_profile
input: #main/reference_genome
is based on: https://workflowhub.eu/workflows/395
keywords: CUTnRUN
license: MIT
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output: #main/Coverage from MACS2 (bigwig)
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output: #main/MACS2 peaks xls
output: #main/MACS2 report
output: #main/MACS2 summits
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output: #main/MarkDuplicates metrics
output: #main/MultiQC on input dataset(s): Stats
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description: Complete ChIP-seq analysis for single-end sequencing data. Processes raw FASTQ files through adapter removal (fastp), alignment to reference genome (Bowtie2), and quality filtering (MAPQ greater than 30). Peak calling with MACS2 uses a fixed extension of 200bp to identify protein-DNA binding sites. Generates alignment files, coverage, peak calls, and quality metrics for downstream analysis.
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input: #main/Effective genome size
input: #main/Normalize profile
input: #main/Percentage of bad quality bases per read
input: #main/Reference genome
input: #main/SR fastq input
is based on: https://workflowhub.eu/workflows/397
keywords: ChIP-seq
license: MIT
output: #main/MACS2 narrowPeak
output: #main/MACS2 peaks
output: #main/MACS2 report
output: #main/MACS2 summits
output: #main/MultiQC on input dataset(s): Stats
output: #main/MultiQC webpage
output: #main/coverage from MACS2
output: #main/filtered BAM
output: #main/mapping stats
producer: https://workflowhub.eu/projects/33
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sd publisher: about.workflowhub.eu
version: 17

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description: Complete ChIP-seq analysis for paired-end sequencing data. Processes raw FASTQ files through adapter removal (fastp), alignment to reference genome (Bowtie2), and stringent quality filtering (MAPQ greater than 30, concordant pairs only). Peak calling with MACS2 optimized for paired-end reads identifies protein-DNA binding sites. Generates alignment files, coverage, peak calls, and quality metrics for downstream analysis.
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input: #main/Normalize profile
input: #main/PE fastq input
input: #main/Percentage of bad quality bases per read
input: #main/Reference genome
is based on: https://workflowhub.eu/workflows/398
keywords: ChIP-seq
license: MIT
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date created: 2026-04-18T02:01:43Z
date modified: 2026-04-18T02:01:43Z
description: Complete ATAC-seq analysis pipeline for paired-end reads. Processes raw FASTQ data through adapter and bad quality removal (fastp), alignment (Bowtie2 end-to-end), and filtering (removes MT reads, discordant pairs, low mapping quality below 30, PCR duplicates). Generates 5' cut site pileups (±100bp), performs peak calling, and quantifies reads in 1kb summit-centered regions. Produces two normalized coverage tracks (per million mapped reads and per million reads in peaks) and fragment length distribution plots for quality assessment.
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input: #main/PE fastq input
input: #main/Percentage of bad quality bases per read
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license: MIT
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output: #main/MarkDuplicates metrics
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producer: https://workflowhub.eu/projects/33
programming language: Galaxy
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StringTie assembly and annotation
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date created: 2020-06-18T23:13:25Z
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description: Alignment, assembly and annotation of RNASEQ reads as well as annotation of generated transcripts.
input: List of Illumina accessions
input: Reference genome
input: Reference genome annotation
input: SARS-CoV-2 proteins
keywords: Alignment, Assembly, Annotation, RNASEQ, StringTie, covid-19
license: Apache-2.0
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Virus genome assembly with Unicycler and Spades.
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date created: 2020-04-10T10:45:00Z
date modified: 2023-01-16T13:39:45Z
description: Virus genome assembly with Unicycler and Spades, The 2 assemblers works in parallel. The graph visualization is made with Bandage. workflow git repository : https://github.com/fjrmoreews/cwl-workflow-SARS-CoV-2/blob/master/Assembly/workflow/assembly-wf-virus.cwl Based on https://github.com/galaxyproject/SARS-CoV-2/blob/master/genomics/2-Assembly/as_wf.png
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input: careful
input: cov_cutoff
input: cov_state
input: fastq1
input: fastq1_type
input: fastq2
input: fastq2_type
input: fastq_file_type
input: iontorrent
input: kmers
input: libraries_fwd_rev
input: libraries_metadata
input: libraries_mono
input: mode
input: nanopore_reads
input: onlyassembler
input: pacbio_reads
input: sanger_reads
input: sc
input: trusted_contigs
input: untrusted_contigs
keywords: covid-19, Assembly
license: MIT
output: all_log_spades
output: assembly_graph_spades
output: assembly_graph_unicycler
output: assembly_graph_with_scaffolds_spades
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output: assembly_info_spades
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output: out_contig_stats_spades
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producer: https://workflowhub.eu/projects/6
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sd publisher: about.workflowhub.eu
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date created: 2026-02-18T03:01:52Z
date modified: 2026-02-18T03:01:52Z
description: Complete RNA-Seq analysis for single-end data: Processes raw FASTQ data through adapter and bad quality removal (fastp), alignment with STAR using ENCODE parameters, gene quantification via multiple methods (STAR and featureCounts), and expression calculation (FPKM with Cufflinks/StringTie, normalized coverage with bedtools). Produces count tables, normalized expression values, and genomic coverage tracks. Supports stranded and unstranded libraries, generating both HTSeq-compatible counts and normalized measures for downstream analysis.
input: Collection of FASTQ files
input: Compute Cufflinks FPKM
input: Compute StringTie FPKM
input: Forward adapter
input: GTF file of annotation
input: GTF with regions to exclude from FPKM normalization with Cufflinks
input: Generate additional QC reports
input: Reference genome
input: Strandedness
input: Use featureCounts for generating count tables
is based on: https://workflowhub.eu/workflows/400
keywords: RNASEQ
license: MIT
output: Counts Table
output: Gene Abundance Estimates from StringTie
output: Genes Expression from Cufflinks
output: Mapped Reads
output: MultiQC HTML report
output: MultiQC stats
output: Small MultiQC HTML report
output: Small MultiQC stats
output: Stranded Coverage
output: Transcripts Expression from Cufflinks
output: Unstranded Coverage
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 13

Lucille Delisle
type: Person

Galaxy
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rnaseq-pe/main
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Lucille Delisle
date created: 2026-02-18T03:01:49Z
date modified: 2026-02-18T03:01:49Z
description: Complete RNA-Seq analysis for paired-end data: Processes raw FASTQ data through adapter and bad quality removal (fastp), alignment with STAR using ENCODE parameters, gene quantification via multiple methods (STAR and featureCounts), and expression calculation (FPKM with Cufflinks/StringTie, normalized coverage with bedtools). Produces count tables, normalized expression values, and genomic coverage tracks. Supports stranded and unstranded libraries, generating both HTSeq-compatible counts and normalized measures for downstream analysis.
input: Collection paired FASTQ files
input: Compute Cufflinks FPKM
input: Compute StringTie FPKM
input: Forward adapter
input: GTF file of annotation
input: GTF with regions to exclude from FPKM normalization with Cufflinks
input: Generate additional QC reports
input: Reference genome
input: Reverse adapter
input: Strandedness
input: Use featureCounts for generating count tables
is based on: https://workflowhub.eu/workflows/401
keywords: RNASEQ
license: MIT
output: Counts Table
output: Gene Abundance Estimates from StringTie
output: Genes Expression from Cufflinks
output: Mapped Reads
output: MultiQC HTML report
output: MultiQC stats
output: Small MultiQC HTML report
output: Small MultiQC stats
output: Stranded Coverage
output: Transcripts Expression from Cufflinks
output: Unstranded Coverage
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 14

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X-omics ACTIONdemonstrator analysis workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Casper de Visser
creator: Anna Niehues
date created: 2023-11-24T13:50:34Z
date modified: 2023-11-24T13:54:50Z
description: This workflow is designed to analyze to a multi-omics data set that comprises genome-wide DNA methylation profiles, targeted metabolomics, and behavioral data of two cohorts that participated in the ACTION Biomarker Study (ACTION, Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies. (Boomsma 2015, Bartels 2018, Hagenbeek 2020, van Dongen 2021, Hagenbeek 2022). The ACTION-NTR cohort consists of twins that are either longitudinally concordant or discordant for childhood aggression. The ACTION-Curium-LUMC cohort consists of children referred to the Dutch LUMC Curium academic center for child and youth psychiatry. With the joint analysis of multi-omics data and behavioral data, we aim to identify substructures in the ACTION-NTR cohort and link them to aggressive behavior. First, the individuals are clustered using Similarity Network Fusion (SNF, Wang 2014), and latent feature dimensions are uncovered using different unsupervised methods including Multi-Omics Factor Analysis (MOFA) (Argelaguet 2018) and Multiple Correspondence Analysis (MCA, Lê 2008, Husson 2017). In a second step, we determine correlations between -omics and phenotype dimensions, and use them to explain the subgroups of individuals from the ACTION-NTR cohort. In order to validate the results, we project data of the ACTION-Curium-LUMC cohort onto the latent dimensions and determine if correlations between omics and phenotype data can be reproduced.
is based on: https://workflowhub.eu/workflows/402
keywords: Multi-omics, Metabolomics, Epigenomics, Behavioral data, FAIR, Nextflow
license: CC-BY-4.0
producer: X-omics
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 8

Kate Farquharson
type: Person

Simon Tang
type: Person

Galaxy
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Genome-assessment-post-assembly
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/139
creator: https://workflowhub.eu/people/200
creator: Kate Farquharson
creator: Simon Tang
date created: 2026-04-17T09:06:28Z
date modified: 2026-04-17T09:08:06Z
description: Post-genome assembly quality control workflow using Quast, BUSCO, Meryl, Merqury and Fasta Statistics, with updates November 2024. Workflow inputs: reads as fastqsanger.gz (not fastq.gz), and primary assembly.fasta. (To change reads format: click on the pencil icon next to the file in the Galaxy history, then "Datatypes", then set "New type" as fastqsanger.gz). Note: the reads should be those that were used for the assembly (i.e., the filtered/cleaned reads), not the raw reads. What it does: Computes read coverage. Runs Quast. Runs Fasta Statistics. Runs Meryl and Merqury. Runs Busco. (New default settings for BUSCO: lineage = eukaryota; for Quast: lineage = eukaryotes, genome = large.) Workflow outputs: Reports assembly stats into a table called metrics.tsv, including selected metrics from Fasta Stats, and read coverage; reports BUSCO versions and dependencies; and displays these tables in the workflow report. Note: a known bug is that sometimes the workflow report text resets to default text. To check and restore: open the workflow in Galaxy for editing. Click on the "Edit Report" icon (top right, pencil icon). Copy and paste the following text into the workflow report, then exit this report page, then save the workflow. # Workflow Execution Report Workflow name: Genome assessment post assembly ## Genome assembly metrics Selected statistics from the workflow outputs. Additional metrics are available in other outputs in the history. ```galaxy history_dataset_display(output="Genome assembly metrics") ``` ## Software Busco version and dependencies: ```galaxy history_dataset_display(output="Busco and dependencies version") ``` ## Galaxy Australia Thanks for using Galaxy! When you use Galaxy Australia to support your publication or project, please acknowledge its use with the following statement: "This work is supported by Galaxy Australia, a service provided by the Australian Biocommons and its partners. The service receives NCRIS funding through Bioplatforms Australi…
input: #main/FASTA contigs - Primary Assembly
input: #main/Reads that were used for the assembly
is based on: https://workflowhub.eu/workflows/403
is part of: https://workflowhub.eu/collections/5
is part of: https://workflowhub.eu/collections/6
keywords: HiFi, hifiasm, QC, Quast, Meryl, Merqury, BUSCO
license: GPL-3.0
output: #main/Busco and dependencies version
output: #main/Busco on input dataset(s): full table
output: #main/Fasta Statistics on input dataset(s): summary stats
output: #main/Genome assembly metrics
output: #main/Genome coverage
output: #main/Merqury on input dataset(s): bed
output: #main/Merqury on input dataset(s): png
output: #main/Merqury on input dataset(s): qv
output: #main/Merqury on input dataset(s): size files
output: #main/Merqury on input dataset(s): stats
output: #main/Merqury on input dataset(s): wig
output: #main/Meryl on input dataset(s): read-db.meryldb
output: #main/Quast on input dataset(s): HTML report
output: #main/Quast on input dataset(s): PDF report
output: #main/Quast on input dataset(s): Log
output: #main/Quast on input dataset(s): tabular report
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producer: https://workflowhub.eu/projects/54
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 10

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type: ComputerLanguage

GBIF data Quality check and filtering workflow Feb-2020
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/51
date created: 2022-11-18T14:39:27Z
date modified: 2024-12-09T08:45:44Z
description: This Galaxy-E workflow was made from the ["Cleaning GBIF data for the use in biogeography" tutorial](https://ropensci.github.io/CoordinateCleaner/articles/Cleaning_GBIF_data_with_CoordinateCleaner.html) and allows to: - Use CoordinateCleaner to automatically flag problematic records - Use GBIF provided meta-data to improve coordinate quality, tailored to your downstream analyses - Use automated cleaning algorithms of CoordinateCleaner to identify problematic contributing datasets - Visualize data on a map
keywords: Biodiversity, Ecology
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/19
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
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GTN Training - Antibiotic Resistance Detection
type: ComputationalWorkflow
type: SoftwareSourceCode
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creator: https://workflowhub.eu/people/360
creator: Willem de Koning
date created: 2022-11-24T13:28:47Z
date modified: 2023-02-13T14:06:44Z
description: Workflow for the GTN training "Antibiotic resistance detection"
input: Plasmids
keywords: Metagenomics
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/12
programming language: Galaxy
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Workflow 3: AMR - SeqSero2/SISTR
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2022-11-24T13:42:42Z
date modified: 2024-09-09T08:23:33Z
description: With this galaxy pipeline you can use Salmonella sp. next generation sequencing results to predict bacterial AMR phenotypes and compare the results against gold standard Salmonella sp. phenotypes obtained from food. This pipeline is based on the work of the National Food Agency of Canada. Doi: [10.3389/fmicb.2020.00549](https://doi.org/10.3389/fmicb.2020.00549) | tool | version | license | | -- | -- | -- | | SeqSero2 | 1.2.1 | [GNU GPL v2.0](https://github.com/denglab/SeqSero2/blob/master/LICENSE) | | BBTools | 39.01 | [MIT License](https://github.com/kbaseapps/BBTools/blob/master/LICENSE) | | SRST2 | 0.2.0 | [BSD License](https://github.com/katholt/srst2/blob/master/LICENSE.txt) | | hamronize | 1.0.3 | [GNU LGPL v3.0](https://github.com/pha4ge/hAMRonization/blob/master/LICENSE.txt) | | SPAdes | v3.15.5 | [GNU GPL v2.0](https://github.com/ablab/spades/blob/main/LICENSE) | | SKESA | 3.0.0 | [Public Domain](https://github.com/ncbi/SKESA/blob/master/LICENSE) | | pilon | 1.1.0 | [GNU GPL v2.0](https://github.com/broadinstitute/pilon/blob/master/LICENSE) | | shovill | 1.0.4 | [GPL-3.0 license](https://github.com/tseemann/shovill/blob/master/LICENSE) | | sistr | 1.1.1 | [Apache-2.0 license](https://github.com/phac-nml/sistr_cmd/blob/master/LICENSE) | | MOB-Recon | 3.0.3 | [Apache-2.0 license](https://github.com/phac-nml/mob-suite/blob/master/LICENSE) |
keywords: Bioinformatics, antimicrobial resistance
license: GPL-3.0
producer: https://workflowhub.eu/projects/110
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Workflow Description Language
type: ComputerLanguage
alternate name: WDL

EmpiricalReads2Map
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/367
date created: 2022-11-29T20:09:43Z
date modified: 2023-01-16T14:04:54Z
description: [![Development](https://img.shields.io/badge/development-active-blue.svg)](https://img.shields.io/badge/development-active-blue.svg) [![Reads2Map](https://circleci.com/gh/Cristianetaniguti/Reads2Map.svg?style=svg)](https://app.circleci.com/pipelines/github/Cristianetaniguti/Reads2Map) ## Reads2Map Reads2Map presents a collection of [WDL workflows](https://openwdl.org/) to build linkage maps from sequencing reads. Each workflow release is described in the [Read2Map releases page](https://github.com/Cristianetaniguti/Reads2Map/releases). The main workflows are the `EmpiricalReads2Map.wdl` and the `SimulatedReads2Map.wdl`. The `EmpiricalReads2Map.wdl` is composed by the `EmpiricalSNPCalling.wdl` that performs the SNP calling, and the `EmpiricalMaps.wdl` that performs the genotype calling and map building in empirical reads. The `SimulatedReads2Map.wdl` simulates Illumina reads for RADseq, exome, or WGS data and performs the SNP and genotype calling and genetic map building. By now, [GATK](https://github.com/broadinstitute/gatk), [Freebayes](https://github.com/ekg/freebayes) are included for SNP calling; [updog](https://github.com/dcgerard/updog), [polyRAD](https://github.com/lvclark/polyRAD), [SuperMASSA](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030906) for dosage calling; and [OneMap](https://github.com/augusto-garcia/onemap), and [GUSMap](https://github.com/tpbilton/GUSMap) for linkage map build. ![math_meth2](https://user-images.githubusercontent.com/7572527/203172239-e4d2d857-84e2-48c5-bb88-01052a287004.png) ## How to use Multiple systems are available to run WDL workflows such as Cromwell, miniWDL, and dxWDL. See further information in the [openwdl documentation](https://github.com/openwdl/wdl#execution-engines). To run a pipeline, first navigate to [Reads2Map releases page](https://github.com/Cristianetaniguti/Reads2Map/releases), search for the pipeline tag you which to run, and download the pipeline’s assets (the WDL workflow, the JSON…
keywords: WDL, linkage_map, variant_calling
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/113
programming language: Workflow Description Language
sd publisher: about.workflowhub.eu
version: 1

Assembly using Tophat2 and annotation
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-06-18T23:17:39Z
date modified: 2023-02-13T14:06:46Z
description: Alignment, assembly and annotation of generated transcripts from RNASEQ reads.
input: List of Illumina accessions
input: Reference annotation
input: Reference genome.
input: SARS-CoV-2 proteins
keywords: Alignment, Assembly, Annotation, Tophat2, RNASEQ, covid-19
license: Apache-2.0
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producer: https://workflowhub.eu/projects/3
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Workflow Description Language
type: ComputerLanguage
alternate name: WDL

SimulatedReads2Map
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/367
date created: 2022-11-29T20:17:01Z
date modified: 2023-01-16T14:04:54Z
description: [![Development](https://img.shields.io/badge/development-active-blue.svg)](https://img.shields.io/badge/development-active-blue.svg) [![Reads2Map](https://circleci.com/gh/Cristianetaniguti/Reads2Map.svg?style=svg)](https://app.circleci.com/pipelines/github/Cristianetaniguti/Reads2Map) ## Reads2Map Reads2Map presents a collection of [WDL workflows](https://openwdl.org/) to build linkage maps from sequencing reads. Each workflow release is described in the [Read2Map releases page](https://github.com/Cristianetaniguti/Reads2Map/releases). The main workflows are the `EmpiricalReads2Map.wdl` and the `SimulatedReads2Map.wdl`. The `EmpiricalReads2Map.wdl` is composed by the `EmpiricalSNPCalling.wdl` that performs the SNP calling, and the `EmpiricalMaps.wdl` that performs the genotype calling and map building in empirical reads. The `SimulatedReads2Map.wdl` simulates Illumina reads for RADseq, exome, or WGS data and performs the SNP and genotype calling and genetic map building. By now, [GATK](https://github.com/broadinstitute/gatk), [Freebayes](https://github.com/ekg/freebayes) are included for SNP calling; [updog](https://github.com/dcgerard/updog), [polyRAD](https://github.com/lvclark/polyRAD), [SuperMASSA](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030906) for dosage calling; and [OneMap](https://github.com/augusto-garcia/onemap), and [GUSMap](https://github.com/tpbilton/GUSMap) for linkage map build. ![math_meth2](https://user-images.githubusercontent.com/7572527/203172239-e4d2d857-84e2-48c5-bb88-01052a287004.png) ## How to use Multiple systems are available to run WDL workflows such as Cromwell, miniWDL, and dxWDL. See further information in the [openwdl documentation](https://github.com/openwdl/wdl#execution-engines). To run a pipeline, first navigate to [Reads2Map releases page](https://github.com/Cristianetaniguti/Reads2Map/releases), search for the pipeline tag you which to run, and download the pipeline’s assets (the WDL workflow, the JSON…
keywords: linkage_map, variant_calling, WDL, reads_simulation
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/113
programming language: Workflow Description Language
sd publisher: about.workflowhub.eu
version: 1

ParslRNA-seq Scientific Workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Luiz Gadelha
creator: https://workflowhub.eu/people/296
creator: Lucas Cruz
date created: 2022-12-06T19:17:17Z
date modified: 2023-01-16T14:04:54Z
description: # RNA-seq Scientific Workflow Workflow for RNA sequencing using the Parallel Scripting Library - Parsl. **Reference:** Cruz, L., Coelho, M., Terra, R., Carvalho, D., Gadelha, L., Osthoff, C., & Ocaña, K. (2021). *Workflows* Científicos de RNA-Seq em Ambientes Distribuídos de Alto Desempenho: Otimização de Desempenho e Análises de Dados de Expressão Diferencial de Genes. In *Anais do XV Brazilian e-Science Workshop*, p. 57-64. Porto Alegre: SBC. DOI: https://doi.org/10.5753/bresci.2021.15789 ## Requirements In order to use RNA-seq Workflow the following tools must be available: - [Bowtie2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) You can install Bowtie2 by running: > bowtie2-2.3.5.1-linux-x86_64.zip Or > sudo yum install bowtie2-2.3.5-linux-x86_64 - [Samtools](http://www.htslib.org/) Samtools is a suite of programs for interacting with high-throughput sequencing data. - [Picard](https://github.com/broadinstitute/picard) Picard is a set of Java command line tools for manipulating high-throughput sequencing (HTS) data and formats. - [HTSeq](https://htseq.readthedocs.io/en/master/) HTSeq is a native Python library that folows conventions of many Python packages. You can install it by running: > pip install HTSeq HTSeq uses [NumPy](https://numpy.org/), [Pysam](https://github.com/pysam-developers/pysam) and [matplotlib](https://matplotlib.org/). Be sure this tools are installed. - [R](https://www.r-project.org/) To use [DESEq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) script make sure R language is also installed. You can install it by running: > sudo apt install r-base - [Parsl - Parallel Scripting Library](https://parsl.readthedocs.io/en/stable/index.html) The recommended way to install Parsl is the suggest approach from Parsl's documentation: > python3 -m pip install parsl - [Python (version >= 3.5)](https://www.python.org/) To use Parsl, you need Python 3.5 or above. You also need Python to use HTSeq, so you should load only one P…
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/115
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

GTF File
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KO Fastq Files
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WT Fastq Files
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RNAseq_UMG_SDumont_v1
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/296
date created: 2022-12-06T19:22:16Z
date modified: 2023-02-13T14:05:45Z
description: RNAseq workflow UMG: Here we introduce a scientific workflow implementing several open-source software executed by Galaxy parallel scripting language in an high-performance computing environment. We have applied the workflow to a single-cardiomyocyte RNA-seq data retrieved from Gene Expression Omnibus database. The workflow allows for the analysis (alignment, QC, sort and count reads, statistics generation) of raw RNA-seq data and seamless integration of differential expression results into a configurable script code.
input: GTF File
input: KO Fastq Files
input: WT Fastq Files
license: CC-BY-4.0
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producer: https://workflowhub.eu/projects/115
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

Mothra
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2022-12-14T16:03:30Z
date modified: 2023-01-16T14:04:58Z
description: Example workflow which allows the use of Mothra Accepts (e.g.) [these](https://github.com/machine-shop/mothra-data/tree/main/test_images) input files, bundled as a collection.
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/72
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

ReMM score
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Max Schubach
date created: 2023-01-03T09:09:05Z
date modified: 2023-01-16T14:04:58Z
description: The Regulatory Mendelian Mutation (ReMM) score was created for relevance prediction of non-coding variations (SNVs and small InDels) in the human genome (GRCh37) in terms of Mendelian diseases. This project updates the ReMM score for the genome build GRCh38 and combines GRCh37 and GRCh38 into one workflow. ## Pre-requirements ### Conda We use Conda as software and dependency management tool. Conda installation guidelines can be found here: https://conda.io/projects/conda/en/latest/user-guide/install/index.html ### Additional programs These programs are used during the workflow. They usually need to be compiled, however, the repository already contains the executables or generated files. - [AttributeDB](https://github.com/visze/attributedb) - [Jannovar](https://github.com/charite/jannovar) - [parSMURF](https://github.com/AnacletoLAB/parSMURF) ### Snakemake The workflow is managed by Snakemake - a workflow management system used to create reproducible and scalable data analyses. To install Snakemake as well as all other required packages, you need to create a working environment according to the description in the file env/ReMM.yaml. For that, first Clone the repository ``` git clone https://github.com/kircherlab/ReMM cd ReMM ``` Create a working environment and activate it ``` conda env create -n ReMM --file workflow/envs/ReMM.yaml conda activate ReMM ``` All paths are relative to the Snakemake file so you do not need to change any path variables. Additionally, Snakemake creates all missing directories, so no need to create any aditional folders either. ## Workflow The workflow consists of four main parts: - Download of feature data - Data processing and cleaning - Model training and validation - Calculation of ReMM for the whole genome The `workflow` folder contains a graph of the workflow and more detailed information on the most important steps. To launch a snakemake workflow, you need to tell snakemake which file you want to generate. We defined all rules for mul…
keywords: non-coding, pathogenicity score, variant pathogenicity prediction, Snakemake, ReMM, Regulatory Mendelian Mutation score
license: MIT
producer: KircherLab
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

contour_levels
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conforms to: 1.0-RELEASE

dec
type: FormalParameter
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do_cone_search
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level_threshold
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ra
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radius
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t1
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t2
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image
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skymap_files
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Gravitational Wave source Cone Search
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Denys Savchenko
date created: 2023-01-11T12:22:37Z
date modified: 2023-01-16T14:05:00Z
input: contour_levels
input: dec
input: do_cone_search
input: level_threshold
input: ra
input: radius
input: t1
input: t2
is part of: https://workflowhub.eu/collections/13
keywords: astronomy, Gravitational Waves, FAIR workflows
license: CC-BY-4.0
output: asciicat
output: contours
output: image
output: skymap_files
producer: ODA
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

GEN-ERA toolbox
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Luc Cornet
date created: 2023-01-13T14:30:04Z
date modified: 2023-03-14T14:35:27Z
description: # Github: https://github.com/Lcornet/GENERA # BCCM GEN-ERA tools repository Please visit the wiki for tutorials and access to the tools: https://github.com/Lcornet/GENERA/wiki # NEWS Mantis is now installed in a singularity container for the Metabolic workflow (install is no longer necessary). # Information about the GEN-ERA project Please visit https://bccm.belspo.be/content/bccm-collections-genomic-era # Publications 1. ToRQuEMaDA: tool for retrieving queried Eubacteria, metadata and dereplicating assemblies. Léonard, R. R., Leleu, M., Vlierberghe, M. V., Cornet, L., Kerff, F., and Baurain, D. (2021). PeerJ 9, e11348. doi:10.7717/peerj.11348. https://peerj.com/articles/11348/ 2. The taxonomy of the Trichophyton rubrum complex: a phylogenomic approach. Cornet, L., D’hooge, E., Magain, N., Stubbe, D., Packeu, A., Baurain, D., and Becker P. (2021). Microbial Genomics 7, 000707. doi:10.1099/mgen.0.000707. https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000707 3. ORPER: A Workflow for Constrained SSU rRNA Phylogenies. Cornet, L., Ahn, A.-C., Wilmotte, A., and Baurain, D. (2021). Genes 12, 1741. doi:10.3390/genes12111741. https://www.mdpi.com/2073-4425/12/11/1741/html 4. AMAW: automated gene annotation for non-model eukaryotic genomes. Meunier, L., Baurain, D., Cornet, L. (2021) https://www.biorxiv.org/content/10.1101/2021.12.07.471566v1 5. Phylogenomic analyses of Snodgrassella isolates from honeybees and bumblebees reveals taxonomic and functional diversity. Cornet, L., Cleenwerck, I., Praet, J., Leonard, R., Vereecken, N.J., Michez, D., Smagghe, G., Baurain, D., Vandamme, P. (2021) https://www.biorxiv.org/content/10.1101/2021.12.10.472130v1 6. Contamination detection in genomic data: more is not enough. Cornet, L & Baurain, D (2022) Genome Biology. 2022;23:60. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02619-9 7. The GEN-ERA toolbox: unified and reproducible workflows for research in microbial genomics Cornet, L., Dur…
license: LGPL-3.0
producer: BCCM_ULC
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Patrick Sorn
type: Person

Thomas Bukur
type: Person

Nextflow
type: ComputerLanguage

CoVigator pipeline: variant detection pipeline for Sars-CoV-2 (and other viruses...)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Pablo Riesgo Ferreiro
creator: Patrick Sorn
creator: Thomas Bukur
date created: 2023-01-17T15:06:13Z
date modified: 2023-01-17T15:06:13Z
description: ![CoVigator logo](images/CoVigator_logo_txt_nobg.png "CoVigator logo") # CoVigator pipeline: variant detection pipeline for Sars-CoV-2 [![DOI](https://zenodo.org/badge/374669617.svg)](https://zenodo.org/badge/latestdoi/374669617) [![Run tests](https://github.com/TRON-Bioinformatics/covigator-ngs-pipeline/actions/workflows/automated_tests.yml/badge.svg?branch=master)](https://github.com/TRON-Bioinformatics/covigator-ngs-pipeline/actions/workflows/automated_tests.yml) [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-Nextflow-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://www.nextflow.io/) [![License](https://img.shields.io/badge/license-MIT-green)](https://opensource.org/licenses/MIT) The Covigator pipeline processes SARS-CoV-2 FASTQ or FASTA files into annotated and normalized analysis ready VCF files. It also classifies samples into lineages using pangolin. The pipeline is implemented in the Nextflow framework (Di Tommaso, 2017), it is a stand-alone pipeline that can be used independently of the CoVigator dashboard and knowledge base. Although it is configured by default for SARS-CoV-2 it can be employed for the analysis of other microbial organisms if the required references are provided. The result of the pipeline is one or more annotated VCFs with the list of SNVs and indels ready for analysis. The results from the CoVigator pipeline populate our CoVigator dashboard [https://covigator.tron-mainz.de](https://covigator.tron-mainz.de) **Table of Contents** 1. [Two pipelines in one](#id1) 2. [Implementation](#id2) 3. [How to run](#id3) 4. [Understanding the output](#id4) 6. [Annotation resources](#id5) 7. [Future work](#id6) 8. [Bibliography](#id7) ## Two pipelines in one In CoVigator we analyse samples from two different formats, FASTQ files (e.g.: as provided by the European Nucleotide Archive) and FASTA files containing a consensus assembly. While from the first we get the raw reads, from the second we obtain already assembled genomes…
keywords: Bioinformatics, SARS-CoV-2, covid-19, variant calling, Nextflow
license: MIT
producer: https://workflowhub.eu/projects/124
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

TronFlow alignment pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-01-17T16:51:42Z
date modified: 2023-01-17T16:51:42Z
description: # TronFlow alignment pipeline ![GitHub tag (latest SemVer)](https://img.shields.io/github/v/release/tron-bioinformatics/tronflow-bwa?sort=semver) [![Run tests](https://github.com/TRON-Bioinformatics/tronflow-bwa/actions/workflows/automated_tests.yml/badge.svg?branch=master)](https://github.com/TRON-Bioinformatics/tronflow-bwa/actions/workflows/automated_tests.yml) [![DOI](https://zenodo.org/badge/327943420.svg)](https://zenodo.org/badge/latestdoi/327943420) [![License](https://img.shields.io/badge/license-MIT-green)](https://opensource.org/licenses/MIT) [![Powered by Nextflow](https://img.shields.io/badge/powered%20by-Nextflow-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://www.nextflow.io/) The TronFlow alignment pipeline is part of a collection of computational workflows for tumor-normal pair somatic variant calling. Find the documentation here [![Documentation Status](https://readthedocs.org/projects/tronflow-docs/badge/?version=latest)](https://tronflow-docs.readthedocs.io/en/latest/?badge=latest) This pipeline aligns paired and single end FASTQ files with BWA aln and mem algorithms and with BWA mem 2. For RNA-seq STAR is also supported. To increase sensitivity of novel junctions use `--star_two_pass_mode` (recommended for RNAseq variant calling). It also includes an initial step of read trimming using FASTP. ## How to run it Run it from GitHub as follows: ``` nextflow run tron-bioinformatics/tronflow-alignment -profile conda --input_files $input --output $output --algorithm aln --library paired ``` Otherwise download the project and run as follows: ``` nextflow main.nf -profile conda --input_files $input --output $output --algorithm aln --library paired ``` Find the help as follows: ``` $ nextflow run tron-bioinformatics/tronflow-alignment --help N E X T F L O W ~ version 19.07.0 Launching `main.nf` [intergalactic_shannon] - revision: e707c77d7b Usage: nextflow main.nf --input_files input_files [--reference reference.fasta] Input: * input_fastq1: th…
is part of: https://workflowhub.eu/collections/7
keywords: Alignment, BWA, STAR, Bioinformatics, fastp
license: MIT
producer: https://workflowhub.eu/projects/124
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

TronFlow BAM preprocessing pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-01-17T16:54:14Z
date modified: 2023-01-17T16:54:14Z
description: # TronFlow BAM preprocessing pipeline ![GitHub tag (latest SemVer)](https://img.shields.io/github/v/release/tron-bioinformatics/tronflow-bam-preprocessing?sort=semver) [![Automated tests](https://github.com/TRON-Bioinformatics/tronflow-bam-preprocessing/actions/workflows/automated_tests.yml/badge.svg)](https://github.com/TRON-Bioinformatics/tronflow-bam-preprocessing/actions/workflows/automated_tests.yml) [![DOI](https://zenodo.org/badge/358400957.svg)](https://zenodo.org/badge/latestdoi/358400957) [![License](https://img.shields.io/badge/license-MIT-green)](https://opensource.org/licenses/MIT) [![Powered by Nextflow](https://img.shields.io/badge/powered%20by-Nextflow-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://www.nextflow.io/) The TronFlow BAM preprocessing pipeline is part of a collection of computational workflows for tumor-normal pair somatic variant calling. These workflows are implemented in the Nextflow (Di Tommaso, 2017) framework. Find the documentation here [![Documentation Status](https://readthedocs.org/projects/tronflow-docs/badge/?version=latest)](https://tronflow-docs.readthedocs.io/en/latest/?badge=latest) The aim of this workflow is to preprocess BAM files based on Picard and GATK (DePristo, 2011) best practices. ## Background In order to have a variant calling ready BAM file there are a number of operations that need to be applied on the BAM. This pipeline depends on the particular variant caller, but there are some common operations. GATK has been providing a well known best practices document on BAM preprocessing, the latest best practices for GATK4 (https://software.broadinstitute.org/gatk/best-practices/workflow?id=11165) does not perform anymore realignment around indels as opposed to best practices for GATK3 (https://software.broadinstitute.org/gatk/documentation/article?id=3238). This pipeline is based on both Picard and GATK. These best practices have been implemented a number of times, see for instance this implementation …
is part of: https://workflowhub.eu/collections/7
keywords: Bioinformatics, GATK4, sambamba
license: MIT
producer: https://workflowhub.eu/projects/124
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Nucleoli segmentation using CellProfiler (EOSC-Life D6)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-11-03T22:12:24Z
date modified: 2023-07-03T10:15:31Z
description: This workflow has been created as part of Demonstrator 6 of the project EOSC-Life (within WP3) and is focused on reusing publicly available RNAi screens to gain insights into the nucleolus biology. The workflow downloads images from the Image Data Resource (IDR), performs object segmentation (of nuclei and nucleoli) and feature extraction of the images and objects identified. Tutorial: https://training.galaxyproject.org/training-material/topics/imaging/tutorials/tutorial-CP/tutorial.html
is based on: https://workflowhub.eu/workflows/41
keywords: CellProfiler, Galaxy, image processing, imaging
license: Apache-2.0
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producer: https://workflowhub.eu/projects/1
producer: https://workflowhub.eu/projects/167
producer: EOSC-Life WP3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 2

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hic-hicup-cooler/hic-fastq-to-cool-hicup-cooler
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type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0002-1964-4960
date created: 2023-09-16T02:01:43Z
date modified: 2026-04-20T01:02:10Z
description: This workflow take as input a collection of paired fastq. It uses HiCUP to go from fastq to validPair file. The pairs are filtered for MAPQ and sorted by cooler to generate a tabix dataset. Cooler is used to generate a balanced cool file to the desired resolution.
input: #main/Bin size in bp
input: #main/Interactions to consider to calculate weights in normalization step
input: #main/No fill-in
input: #main/PE fastq input
input: #main/Restriction enzyme
input: #main/genome name
input: #main/minimum MAPQ
input: #main/region for matrix plotting
is based on: https://workflowhub.eu/workflows/420
keywords: Hi-C
license: MIT
output: #main/HiCUP report (html)
output: #main/HiCUP report (txt)
output: #main/matrix with iced values
output: #main/matrix with raw values
output: #main/plot with pyGenomeTracks
output: #main/valid pairs filtered and sorted
output: #main/valid pairs in juicebox format
output: #main/valid pairs in juicebox format MAPQ filtered
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 3

R markdown
type: ComputerLanguage
alternate name: RMD

Apis-wings-EU: A workflow for morphometric identification of honey bees from Europe
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Fani Hatjina
creator: Slađan Rašić
creator: Maria Bouga
creator: Zlatko Puškadija
creator: M. Alice Pinto
creator: Andrzej Oleksa
creator: Pedro João Rodrigues
creator: Leonidas Charistos
creator: Irfan Kandemir
creator: Marin Kovačić
creator: https://orcid.org/0000-0002-3898-7029
creator: Eliza Căuia
creator: Szilvia Kusza
creator: Adrian Siceanu
creator: Janez Prešern
date created: 2023-01-25T07:16:25Z
date modified: 2023-02-28T13:04:32Z
description: We present an R script that describes the workflow for analysing honey bee (Apis mellifera) wing shape. It is based on a large dataset of wing images and landmark coordinates available at Zenodo: https://doi.org/10.5281/zenodo.7244070. The dataset can be used as a reference for the identification of unknown samples. As unknown samples, we used data from Nawrocka et al. (2018), available at Zenodo: https://doi.org/10.5281/zenodo.7567336. Among others, the script can be used to identify the geographic origin of unknown samples and therefore assist in the monitoring and conservation of honey bee biodiversity in Europe.
license: CC0-1.0
producer: https://workflowhub.eu/projects/121
programming language: R markdown
sd publisher: about.workflowhub.eu
version: 1

Climate - Climate 101
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/49
date created: 2020-06-29T14:00:25Z
date modified: 2023-01-16T13:43:36Z
description: The tutorial for this workflow can be found on [Galaxy Training Network](https://training.galaxyproject.org/training-material/topics/climate/tutorials/climate-101/tutorial.html)
input: tg_ens_mean_0_1deg_reg_v20_0e_Paris_daily_csv
input: ts_cities_csv
keywords: GTN, Climate
license: MIT
producer: https://workflowhub.eu/projects/12
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

workdir_array
type: FormalParameter
conforms to: 1.0-RELEASE

final_result
type: FormalParameter
conforms to: 1.0-RELEASE

Nextflow
type: ComputerLanguage

GermlineStructuralV-nf
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Marina Kennerson
creator: Sarah Beecroft
creator: https://workflowhub.eu/people/123
creator: https://workflowhub.eu/people/141
date created: 2023-01-31T23:40:40Z
date modified: 2023-12-18T05:36:07Z
description: GermlineStructuralV-nf is a pipeline for identifying structural variant events in human Illumina short read whole genome sequence data. GermlineStructuralV-nf identifies structural variant and copy number events from BAM files using [Manta](https://github.com/Illumina/manta/blob/master/docs/userGuide/README.md#de-novo-calling), [Smoove](https://github.com/brentp/smoove), and [TIDDIT](https://github.com/SciLifeLab/TIDDIT). Variants are then merged using [SURVIVOR](https://github.com/fritzsedlazeck/SURVIVOR), and annotated by [AnnotSV](https://pubmed.ncbi.nlm.nih.gov/29669011/). The pipeline is written in Nextflow and uses Singularity/Docker to run containerised tools.
keywords: Bioinformatics, Annotation, Genomics, Nextflow, rare diseases, variant_calling, structural variants, manta, smoove, tiddit, annotsv, survivor
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/43
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

Reconstructing raw tomography data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Felicita Gernhardt
date created: 2023-02-02T13:46:59Z
date modified: 2023-02-02T13:50:16Z
description: # Snakemake workflow: Reconstructing raw tomography data A Snakemake worfklow for tomographically reconstructing raw data using [tomopy](https://tomopy.readthedocs.io/en/stable/). ## Installation First download this repo and navigate to it ```bash git clone https://codebase.helmholtz.cloud/gernha62/reconstructing-raw-tomography-data.git ``` ```bash cd /path/to/repo ``` (Optional) Download the example folder with: ```bash wget -m -np https://doi2.psi.ch/datasets/das/work/p15/p15869/compression/MI04_02/tif ``` Create a virtual environment and install all necessary packages (requires conda): ```bash conda env create --name reconstr_env --file workflow/envs/reconstr.yml ``` Activate the new virtual environment: ```bash conda activate reconstr_env ``` ## Configuration To configure the workflow, adapt the config file found at `config/config.yaml` . The config looks as follows: ```yaml number_of_darks: 50 number_of_flats: 100 number_of_projections: 501 rotation_center: 508.77 raw_data: MI04_02: doi2.psi.ch/datasets/das/work/p15/p15869/compression/MI04_02/tif ``` In the config, adjust `number_of_darks`, `number_of_flats`, `number_of_projections` and `rotation_center` to the number of darks, flats, projections and the rotation center of your dataset. The necessary information can usually be found in the .log file of the folder that contains the raw data. `MI04_02: doi2.psi.ch/datasets/das/work/p15/p15869/compression/MI04_02/tif` denotes the path to the example folder used for reconstruction and the keyword `MI04_02` will be used to name the output (e.g. in this case the output folder will be named `recon_dir_MI04_02`). Replace the examle path with the path to the dataset you want to reconstruct. Additionally, if you want the name of the output folder to have a different suffix, replace the keyword `MI04_02` with a name you prefer. ## Run the workflow If the .tif files contain a numerical prefix that is not separated from the actual image index, it is best to first rename the…
keywords: Reconstruction, Tomography
license: MIT
producer: Computational Science at HZDR
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

TF-Prioritizer
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-02-02T14:42:11Z
date modified: 2023-02-02T14:42:11Z
description: # 1. About TF-Prioritizer This pipeline gives you a full analysis of nfcore chromatine accessibility peak data (ChIP-Seq, ATAC-Seq or DNAse-Seq) and nfcore RNA-seq count data. It performs DESeq2, TEPIC and DYNAMITE including all preprocessing and postprocessing steps necessary to transform the data. It also gives you plots for deep analysis of the data. The general workflow is sketched in the images below: ## Graphical abstract: ![Graphical abstrat](https://raw.githubusercontent.com/biomedbigdata/TF-Prioritizer/master/media/graphicalAbstract.png) ## Technical workflow: ![Technical workflow](https://github.com/biomedbigdata/TF-Prioritizer/raw/master/media/technicalWorkflow.png) # 2. License and Citing TF-Prioritizer is distributed under the [GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.en.html). The Graphical Abstract and the Technical Workflow was created using [biorender.com](https://biorender.com/). # 3. Usage The software can be executed using docker. For the following command, only [python3](https://www.python.org/downloads/), [curl](https://curl.se/download.html) and [docker](https://docs.docker.com/get-docker/) are required. Explanations about the configs can be found in the [config readme](https://github.com/biomedbigdata/TF-Prioritizer/blob/master/configTemplates/README.md). ```bash curl -s https://raw.githubusercontent.com/biomedbigdata/TF-Prioritizer/master/docker.py | python3 - -c [config_file] -o [output_dir] -t [threads] ``` Note, that for this approach an internet connection is required. The docker image will be downloaded from [DockerHub](https://hub.docker.com/r/nicotru/tf-prioritizer) on the first execution as well as with every update we release. Furthermore, the wrapper script will be fetched from GitHub with every execution. If curl is not available (for example if you are using windows), or you want to be able to execute the software without an internet connection, you can download the wrapper script from [here](https://raw.g…
license: LGPL-3.0
producer: Big data in biomedicine
sd publisher: about.workflowhub.eu
version: 1

Other data (fasterq-dump)
type: FormalParameter
conforms to: 1.0-RELEASE

Pair-end data (fasterq-dump)
type: FormalParameter
conforms to: 1.0-RELEASE

Single-end data (fasterq-dump)
type: FormalParameter
conforms to: 1.0-RELEASE

fasterq-dump log
type: FormalParameter
conforms to: 1.0-RELEASE

rnaviralSPAdes on input dataset(s): Contigs
type: FormalParameter
conforms to: 1.0-RELEASE

Galaxy
type: ComputerLanguage

extract SRA + viralRNAspades (PE)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-02-10T10:05:10Z
date modified: 2023-02-10T10:05:10Z
description: extract 1 Id from SRA and assume it is PE as input to viralRNASpades.
license: CC-BY-4.0
output: Other data (fasterq-dump)
output: Pair-end data (fasterq-dump)
output: Single-end data (fasterq-dump)
output: fasterq-dump log
output: rnaviralSPAdes on input dataset(s): Contigs
producer: https://workflowhub.eu/projects/27
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

sqtlseeker2-nf
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/403
creator: https://workflowhub.eu/people/405
date created: 2023-02-15T11:54:54Z
date modified: 2023-02-15T12:02:21Z
description: # sqtlseeker2-nf [![nextflow](https://img.shields.io/badge/nextflow-%E2%89%A50.27.0-blue.svg)](http://nextflow.io) [![CI-checks](https://github.com/guigolab/sqtlseeker2-nf/actions/workflows/ci.yaml/badge.svg)](https://github.com/guigolab/sqtlseeker2-nf/actions/workflows/ci.yaml) A pipeline for splicing quantitative trait loci (sQTL) mapping. The pipeline performs the following analysis steps: * Index the genotype file * Preprocess the transcript expression data * Test for association between splicing ratios and genetic variants in *cis* (nominal pass) * Obtain an empirical P-value for each phenotype (permutation pass, optional) * Control for multiple testing For details on each step, please read [sQTLseekeR2](https://github.com/guigolab/sQTLseekeR2) documentation. The pipeline uses [Nextflow](http://www.nextflow.io) as the execution backend. Please check [Nextflow documentation](http://www.nextflow.io/docs/latest/index.html) for more information. ## Requirements - Unix-like operating system (Linux, MacOS, etc.) - Java 8 or later - [Docker](https://www.docker.com/) (v1.10.0 or later) or [Singularity](http://singularity.lbl.gov) (v2.5.0 or later) ## Quickstart (~2 min) 1. Install Nextflow: ``` curl -fsSL get.nextflow.io | bash ``` 2. Make a test run: ``` ./nextflow run guigolab/sqtlseeker2-nf -with-docker ``` **Note**: set `-with-singularity` to use Singularity instead of Docker. ## Pipeline usage Launching the pipeline with the `--help` parameter shows the help message: ``` nextflow run sqtlseeker2-nf --help ``` ``` N E X T F L O W ~ version 0.27.2 Launching `sqtlseeker2.nf` [admiring_lichterman] - revision: 28c86caf1c sqtlseeker2-nf ~ A pipeline for splicing QTL mapping ---------------------------------------------------- Run sQTLseekeR2 on a set of data. Usage: sqtlseeker2-nf [options] Options: --genotype GENOTYPE_FILE the genotype file --trexp EXPRESSION_FILE the transcript expression file --metadata METADATA_FILE the metadata file --genes GENES_FILE the gene loca…
keywords: QTL mapping, rna-seq, SNPs, Nextflow, Alternative splicing
license: GPL-3.0
producer: Guigó lab
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

mvgwas-nf
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/403
creator: https://workflowhub.eu/people/405
date created: 2023-02-15T11:58:32Z
date modified: 2023-02-15T12:09:57Z
description: # mvgwas-nf [![nextflow](https://img.shields.io/badge/nextflow-%E2%89%A520.04.1-blue.svg)](http://nextflow.io) [![CI-checks](https://github.com/guigolab/sqtlseeker2-nf/actions/workflows/ci.yaml/badge.svg)](https://github.com/guigolab/sqtlseeker2-nf/actions/workflows/ci.yaml) A pipeline for multi-trait genome-wide association studies (GWAS) using [MANTA](https://github.com/dgarrimar/manta). The pipeline performs the following analysis steps: * Split genotype file * Preprocess phenotype and covariate data * Test for association between phenotypes and genetic variants * Collect summary statistics The pipeline uses [Nextflow](http://www.nextflow.io) as the execution backend. Please check [Nextflow documentation](http://www.nextflow.io/docs/latest/index.html) for more information. ## Requirements - Unix-like operating system (Linux, MacOS, etc.) - Java 8 or later - [Docker](https://www.docker.com/) (v1.10.0 or later) or [Singularity](http://singularity.lbl.gov) (v2.5.0 or later) ## Quickstart (~2 min) 1. Install Nextflow: ``` curl -fsSL get.nextflow.io | bash ``` 2. Make a test run: ``` nextflow run dgarrimar/mvgwas-nf -with-docker ``` **Notes**: move the `nextflow` executable to a directory in your `$PATH`. Set `-with-singularity` to use Singularity instead of Docker. (*) Alternatively you can clone this repository: ``` git clone https://github.com/dgarrimar/mvgwas-nf cd mvgwas-nf nextflow run mvgwas.nf -with-docker ``` ## Pipeline usage Launching the pipeline with the `--help` parameter shows the help message: ``` nextflow run mvgwas.nf --help ``` ``` N E X T F L O W ~ version 20.04.1 Launching `mvgwas.nf` [amazing_roentgen] - revision: 56125073b7 mvgwas-nf: A pipeline for multivariate Genome-Wide Association Studies ============================================================================================== Performs multi-trait GWAS using using MANTA (https://github.com/dgarrimar/manta) Usage: nextflow run mvgwas.nf [options] Parameters: --pheno PHENOTYPES phenotype…
keywords: GWAS, Multivariate, Non-parametric, Nextflow
license: GPL-3.0
producer: Statistical genetics
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Ang Guo
type: Person

Qian Luo
type: Person

Python
type: ComputerLanguage

Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Ang Guo
creator: Qian Luo
date created: 2023-02-16T00:34:18Z
date modified: 2023-03-08T23:57:45Z
description: # ROIforMSI Source codes for manuscript "Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation" "ExampleWorkflow.ipynb" is a methods document to demonstrate the workflow of our multimodal fusion-based spatial segmentation. "Utilities.py" contains all the tools to implement our method. "gui.py" and "registration_gui.py" are files to implement linear and nonlinear registration. (Licence: GPL-3)
license: AGPL-3.0
producer: Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation
programming language: Python
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

Master of Pores 2
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-02-20T15:48:34Z
date modified: 2023-02-20T15:48:34Z
description: # MoP2- DSL2 version of Master of Pores [![Docker Build Status](https://img.shields.io/docker/automated/biocorecrg/nanopore.svg)](https://cloud.docker.com/u/biocorecrg/repository/docker/biocorecrg/nanopore/builds) [![mop2-CI](https://github.com/biocorecrg/MoP2/actions/workflows/build.yml/badge.svg)](https://github.com/biocorecrg/MoP2/actions/workflows/build.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Nextflow version](https://img.shields.io/badge/Nextflow-21.04.1-brightgreen)](https://www.nextflow.io/) [![Nextflow DSL2](https://img.shields.io/badge/Nextflow-DSL2-brightgreen)](https://www.nextflow.io/) [![Singularity version](https://img.shields.io/badge/Singularity-v3.2.1-green.svg)](https://www.sylabs.io/) [![Docker version](https://img.shields.io/badge/Docker-v20.10.8-blue)](https://www.docker.com/) ![MOP2](https://github.com/biocorecrg/MoP2/blob/main/img/master_red.jpg?raw=true) Inspired by Metallica's [Master Of Puppets](https://www.youtube.com/watch?v=S7blkui3nQc) ## Install Please install nextflow and singularity or docker before. Then download the repo: ``` git clone --depth 1 --recurse-submodules git@github.com:biocorecrg/MOP2.git ``` You can use INSTALL.sh to download the version 3.4.5 of guppy or you can replace it with the version you prefer. Please consider that the support of VBZ compression of fast5 started with version 3.4.X. ``` cd MoP2; sh INSTALL.sh ``` ## Testing You can replace ```-with-singularity``` with ```-with-docker``` if you want to use the docker engine. ``` cd mop_preprocess nextflow run mop_preprocess.nf -with-singularity -bg -profile local > log ``` ## Reference If you use this tool, please cite our papers: ["Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores" Cozzuto L, Delgado-Tejedor A, Hermoso Pulido T, Novoa EM, Ponomarenko J. *N. Methods Mol Biol. 2023*;2624:185-205. doi: 10.1007/978-1-0716-2962-8_13.](https://link.springer.com/pr…
keywords: nanopore, ONT, dRNAseq, Transcriptomics, metatranscriptomics
license: CC-BY-4.0
producer: Bioinformatics Unit @ CRG
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

Allele frequency to call SNV
type: FormalParameter
conforms to: 1.0-RELEASE

Allele frequency to call indel
type: FormalParameter
conforms to: 1.0-RELEASE

Minimum quality score to call base
type: FormalParameter
conforms to: 1.0-RELEASE

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fastp_pool1_html
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fastp_pool1_json
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fastp_pool1_reads
type: FormalParameter
conforms to: 1.0-RELEASE

fastp_pool2_html
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fastp_pool2_json
type: FormalParameter
conforms to: 1.0-RELEASE

fastp_pool2_reads
type: FormalParameter
conforms to: 1.0-RELEASE

filtered_mapping_pool1
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conforms to: 1.0-RELEASE

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type: FormalParameter
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type: FormalParameter
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pox-virus-amplicon/main
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Viktoria Isabel Schwarz
creator: https://workflowhub.eu/people/222
date created: 2025-10-11T02:01:48Z
date modified: 2025-11-27T14:10:00Z
description: A workflow for the analysis of pox virus genomes sequenced as half-genomes (for ITR resolution) in a tiled-amplicon approach
input: Allele frequency to call SNV
input: Allele frequency to call indel
input: Minimum quality score to call base
input: PE Reads Pool1
input: PE Reads Pool2
input: Primer Scheme
input: Reference FASTA
is based on: https://workflowhub.eu/workflows/439
keywords: pox, Virology
license: MIT
output: Mapping of Pool1
output: combined_consensus_multifasta
output: fastp_pool1_html
output: fastp_pool1_json
output: fastp_pool1_reads
output: fastp_pool2_html
output: fastp_pool2_json
output: fastp_pool2_reads
output: filtered_mapping_pool1
output: filtered_mapping_pool2
output: input dataset(s) (sorted)
output: ivar_consensus_out
output: mapping_merged
output: mapping_pool2
output: mapping_stats_pool1
output: mapping_stats_pool2
output: masked_ref_pool1
output: masked_ref_pool2
output: multiqc_pool1_stats
output: multiqc_pool2_stats
output: multiqc_sample_stats
output: per_sample_consensus
output: pool1_primers
output: pool1_quality_report
output: pool2_primers
output: pool2_quality_report
output: qualimap_merged_html
output: qualimap_pool1_raw
output: quality_by_sample_report
output: trimmed_merged_mapping
producer: https://workflowhub.eu/projects/33
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 4

NMR pipe
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-07-22T10:49:00Z
date modified: 2023-01-16T13:43:41Z
description: CWL workflow for NMR spectra Peak Picking The workflow takes as input a series of 2D 1H 15N HSQC NMR spectra and uses nmrpipe tools to convert the spectra in nmrpipe format and performs an automatic peak picking. This test uses a protein MDM2 with different ligands and peptide and generates a peak list with 1H and 15N chemical shift values for each spectrum. The difference among these peak lists can be used to characterize the ligand binding site on the protein.
input: workdir_array
license: Apache-2.0
output: final_result
producer: NMR Workflow
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Harshil Patel
type: Person

Phil Ewels
type: Person

Rickard Hammarén
type: Person

Nextflow
type: ComputerLanguage

Nextflow
type: ComputerLanguage

IGVreport-nf
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/123
creator: https://workflowhub.eu/people/141
date created: 2023-03-21T05:17:30Z
date modified: 2023-03-21T05:17:30Z
description: # IGVreport-nf - [Description](#description) - [Diagram](#diagram) - [User guide](#user-guide) - [Workflow summaries](#workflow-summaries) - [Metadata](#metadata) - [Component tools](#component-tools) - [Required (minimum) inputs/parameters](#required-minimum-inputsparameters) - [Additional notes](#additional-notes) - [Help/FAQ/Troubleshooting](#helpfaqtroubleshooting) - [Acknowledgements/citations/credits](#acknowledgementscitationscredits) ## Description Quickly generate [IGV `.html` reports](https://github.com/igvteam/igv-reports) for a genomic region of interest in the human genome (hg38). Bcftools is used to subset a VCF to a region of interest, the subset VCF is then passed to IGV-reports, which generates a report consisting of a table of genomic sites or regions and associated IGV views for each site. The reports can be opened by any web browser as a static page. ### Diagram ```mermaid graph LR; VCF-->|bcftools view|SubsetVCF; SubsetVCF-->|IGVtools|HTMLreport; AlignmentBAM-->|IGVtools|HTMLreport; ``` ### User guide This workflow uses containers for all steps and can run using Singularity or Docker. It requires Nextflow and either Singularity or Docker be installed. For instructions on installing Nextflow, see their [documentation](https://www.nextflow.io/docs/latest/getstarted.html). **This workflow currently only generates reports for the human reference genome assembly, Hg38.** The workflow runs three processes: 1. The provided VCF file is subset to a region of interest using Bcftools view 2. The Subset VCF file is then indexed using Bcftools index 3. The subset VCF and provided Bam file are used to generate the html report for the region of interest. To start clone this repository: ``` git clone https://github.com/Sydney-Informatics-Hub/IGVreport-nf.git ``` From the IGVreport-nf directory, run the pipeline: ``` nextflow run main.nf --sample \ --bam \ --vcf \ --chr --start --stop ``` This will create a report in a directory titled `./Report`. You can rename…
keywords: Alignment, Genomics, variant calling, mapping
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/30
producer: https://workflowhub.eu/projects/43
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

SNP-Calling Workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-03-21T05:30:15Z
date modified: 2023-03-21T05:30:15Z
description: # SNP-Calling GATK Variant calling pipeline for genomic data using Nextflow [![nextflow](https://img.shields.io/badge/nextflow-%E2%89%A522.04.5-brightgreen.svg)](http://nextflow.io) ## Quickstart Install Nextflow using the following command: curl -s https://get.nextflow.io | bash Index reference genome: `$ bwa index /path/to/reference/genome.fa` `$ samtools faidx /path/to/reference/genome.fa` `$ gatk CreateSequenceDictionary -R /path/to/genome.fa -O genome.dict` Launch the pipeline execution with the following command: nextflow run jdetras/snp-calling -r main -profile docker ## Pipeline Description The variant calling pipeline follows the recommended practices from GATK. The input genomic data are aligned to a reference genome using BWA. The alignemnt files are processed using Picard Tools. Variant calling is done using samtools and GATK. ## Input files The input files required to run the pipeline: * Genomic sequence paired reads, `*_{1,2}.fq.gz` * Reference genome, `*.fa` ## Pipeline parameters ### Usage Usage: `nextflow run jdetras/snp-calling -profile docker [options]` Options: * `--reads` * `--genome` * `--output` Example: `$ nextflow run jdetras/snp-calling -profile docker --reads '/path/to/reads/*_{1,2}.fq.gz' --genome '/path/to/reference/genome.fa' --output '/path/to/output'` #### `--reads` * The path to the FASTQ read files. * Wildcards (*, ?) can be used to declare multiple reads. Use single quotes when wildcards are used. * Default parameter: `$projectDir/data/reads/*_{1,2}.fq.gz` Example: `$ nextflow run jdetras/snp-calling -profile docker --reads '/path/to/reads/*_{1,2}.fq.gz'` #### `--genome` * The path to the genome file in fasta format. * The extension is `.fa`. * Default parameter: `$projectDir/data/reference/genome.fa` Example: `$ nextflow run jdetras/snp-calling -profile docker --genome /path/to/reference/genome.fa` #### `--output` * The path to the directory for the output files. * Default parameter: `$projectDir/output` ## Software * [BWA 0.7.17]…
keywords: variant calling, GATK4, BWA-mem, rice
license: MIT
producer: IRRI Bioinformatics Group
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

GRAVI: Gene Regulatory Analysis using Variable Inputs
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/428
date created: 2023-03-21T05:48:16Z
date modified: 2024-10-24T15:31:14Z
description: # GRAVI: Gene Regulatory Analysis using Variable Inputs This is a `snakemake` workflow for: 1. Performing sample QC 2. Calling ChIP peaks 3. Performing Differential Binding Analysis 4. Comparing results across ChIP targets The minimum required input is one ChIP target with two conditions. Full documentation can be found [here](https://steveped.github.io/GRAVI/) ## Snakemake Implementation The basic workflow is written `snakemake`, requiring at least v7.7, and can be called using the following steps. Firstly, setup the required conda environments ``` snakemake \ --use-conda \ --conda-prefix '/home/steveped/mambaforge/envs/' \ --conda-create-envs-only \ --cores 1 ``` Secondly, create and inspect the rulegraph ``` snakemake --rulegraph > workflow/rules/rulegraph.dot dot -Tpdf workflow/rules/rulegraph.dot > workflow/rules/rulegraph.pdf ``` Finally, the workflow itself can be run using: ``` snakemake \ -p \ --use-conda \ --conda-prefix '/home/steveped/mambaforge/envs/' \ --notemp \ --rerun-triggers mtime \ --keep-going \ --cores 16 ``` Note that this creates common environments able to be called by other workflows and is dependent on the user. For me, my global conda environments are stored in `/home/steveped/mambaforge/envs/`. For other users, this path will need to be modified. If wishing to tidy the directory after a successful run, you can check which non-essential files can be deleted using `snakemake -n --delete-temp-output --cores 1`. If the files earmarked for deletion are considered to be non-essential, they can be deleted by removing the `-n` flag from the above code: `snakemake --delete-temp-output --cores 1`. As the bedgraph files produced by `macs2 callpeak` are typically very large, hence their conversion to bigwig files during the workflow, this step can free a considerable amount of disk space.
keywords: ChIP-seq, BAM, Bioinformatics
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/148
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Magnus Palmblad
type: Person

Nextflow
type: ComputerLanguage

WOMBAT-Pipelines
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Veit Schwämmle
creator: Magnus Palmblad
date created: 2023-09-01T09:01:32Z
date modified: 2025-06-04T13:58:44Z
description: ## Introduction **wombat-p pipelines** is a bioinformatics analysis pipeline that bundles different workflow for the analysis of label-free proteomics data with the purpose of comparison and benchmarking. It allows using files from the [proteomics metadata standard SDRF](https://github.com/bigbio/proteomics-metadata-standard). The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. We used one of the [nf-core](https://nf-co.re/) templates. ## Pipeline summary This work contains four major different workflows for the analysis or label-free proteomics data, originating from LC-MS experiments. 1. [MaxQuant](https://www.maxquant.org/) + [NormalyzerDE](https://normalyzerde.immunoprot.lth.se/) 2. [SearchGui](http://compomics.github.io/projects/searchgui) + [Proline](https://www.profiproteomics.fr/proline/) + [PolySTest](https://bitbucket.org/veitveit/polystest) 3. [Compomics tools](http://compomics.github.io/) + [FlashLFQ](https://github.com/smith-chem-wisc/FlashLFQ) + [MSqRob](https://github.com/statOmics/MSqRob) 4. Tools from the [Trans-Proteomic Pipeline](http://tools.proteomecenter.org/TPP.php) + [ROTS](https://bioconductor.org/packages/release/bioc/html/ROTS.html) Initialization and parameterization of the workflows is based on tools from the [SDRF pipelines](https://github.com/bigbio/sdrf-pipelines), the [ThermoRawFileParser](http://compomics.github.io/projects/ThermoRawFileParser) with our own contributions and additional programs from the wombat-p organizaion [https://github.com/wombat-p/Utilities] as well as our [fork](https://github.com/elixir-proteomics-communit…
is based on: https://workflowhub.eu/workflows/444
keywords: Proteomics, MaxQuant, Proline, Compomics, Trans-Proteomic
license: MIT
producer: ELIXIR Proteomics
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 2

Snakemake
type: ComputerLanguage

ZARP: An automated workflow for processing of RNA-seq data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Zavolan Lab
date created: 2023-03-21T13:07:16Z
date modified: 2023-05-12T15:33:47Z
description: [![ci](https://github.com/zavolanlab/zarp/workflows/CI/badge.svg?branch=dev)](https://github.com/zavolanlab/zarp/actions?query=workflow%3Aci) [![GitHub license](https://img.shields.io/github/license/zavolanlab/zarp?color=orange)](https://github.com/zavolanlab/zarp/blob/dev/LICENSE) [![DOI:10.1101/2021.11.18.469017](http://img.shields.io/badge/DOI-10.1101/2021.11.18.469017-B31B1B.svg)](https://doi.org/10.1101/2021.11.18.469017) # **ZARP** ([Zavolan-Lab](https://www.biozentrum.unibas.ch/research/researchgroups/overview/unit/zavolan/research-group-mihaela-zavolan/) Automated RNA-Seq Pipeline) ...is a generic RNA-Seq analysis workflow that allows users to process and analyze Illumina short-read sequencing libraries with minimum effort. The workflow relies on publicly available bioinformatics tools and currently handles single or paired-end stranded bulk RNA-seq data. The workflow is developed in [Snakemake](https://snakemake.readthedocs.io/en/stable/), a widely used workflow management system in the bioinformatics community. According to the current ZARP implementation, reads are analyzed (pre-processed, aligned, quantified) with state-of-the-art tools to give meaningful initial insights into the quality and composition of an RNA-Seq library, reducing hands-on time for bioinformaticians and giving experimentalists the possibility to rapidly assess their data. Additional reports summarise the results of the individual steps and provide useful visualisations. > **Note:** For a more detailed description of each step, please refer to the [workflow > documentation](https://github.com/zavolanlab/zarp/blob/main/pipeline_documentation.md). ## Requirements The workflow has been tested on: - CentOS 7.5 - Debian 10 - Ubuntu 16.04, 18.04 > **NOTE:** > Currently, we only support **Linux** execution. # Installation ## 1. Clone the repository Go to the desired directory/folder on your file system, then clone/get the repository and move into the respective directory with: ```bash git c…
keywords: Bioinformatics, rna, rna-seq, RNASEQ, NGS, high-throughput
license: Apache-2.0
producer: Zavolan Lab
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

nf-core/rnaseq
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Harshil Patel
creator: Phil Ewels
creator: Rickard Hammarén
date created: 2025-09-03T02:04:50Z
date modified: 2026-04-20T01:02:10Z
description: [![GitHub Actions CI Status](https://github.com/nf-core/rnaseq/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/rnaseq/actions/workflows/nf-test.yml) [![GitHub Actions Linting Status](https://github.com/nf-core/rnaseq/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/rnaseq/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/rnaseq/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.1400710-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.1400710) [![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com) [![Nextflow](https://img.shields.io/badge/version-%E2%89%A524.10.5-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/) [![nf-core template version](https://img.shields.io/badge/nf--core_template-3.3.2-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/3.3.2) [![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/) [![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/) [![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/) [![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/rnaseq) [![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23rnaseq-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/rnaseq)[![Follow on Bluesky](https://img.shields.io/badge/bluesky-%40nf__core-1185fe?labelColor=000000&logo=bluesky)](htt…
is based on: https://workflowhub.eu/workflows/44
keywords: rna, rna-seq
license: MIT
producer: https://workflowhub.eu/projects/15
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 37

Jesse van Dam
type: Person

Peter Schaap
type: Person

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

Reverse read length
type: FormalParameter
conforms to: 1.0-RELEASE

Forward primer
type: FormalParameter
conforms to: 1.0-RELEASE

forward reads
type: FormalParameter
conforms to: 1.0-RELEASE

Reference database
type: FormalParameter
conforms to: 1.0-RELEASE

Reverse read length
type: FormalParameter
conforms to: 1.0-RELEASE

Reverse primer
type: FormalParameter
conforms to: 1.0-RELEASE

reverse reads
type: FormalParameter
conforms to: 1.0-RELEASE

Sample name
type: FormalParameter
conforms to: 1.0-RELEASE

files_to_folder_fastqc
type: FormalParameter
conforms to: 1.0-RELEASE

files_to_folder_ngtax
type: FormalParameter
conforms to: 1.0-RELEASE

Nextflow
type: ComputerLanguage

MGnify raw reads taxonomic profiling pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/27
creator: https://workflowhub.eu/people/355
date created: 2023-03-30T20:49:19Z
date modified: 2023-03-30T20:58:56Z
description: The containerised pipeline for profiling shotgun metagenomic data is derived from the [MGnify](https://www.ebi.ac.uk/metagenomics/) pipeline raw-reads analyses, a well-established resource used for analyzing microbiome data. Key components: - Quality control and decontamination - rRNA and ncRNA detection using Rfam database - Taxonomic classification of SSU and LSU regions - Abundance analysis with mOTUs
keywords: Nextflow, Metagenomics
license: Apache-2.0
producer: https://workflowhub.eu/projects/9
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Snakemake
type: ComputerLanguage

RASflow: RNA-Seq Analysis Snakemake Workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Xiaokang Zhang
date created: 2023-04-06T07:54:59Z
date modified: 2023-04-06T07:54:59Z
description: # RASflow: RNA-Seq Analysis Snakemake Workflow RASflow is a modular, flexible and user-friendly RNA-Seq analysis workflow. RASflow can be applied to both model and non-model organisms. It supports mapping RNA-Seq raw reads to both genome and transcriptome (can be downloaded from public database or can be homemade by users) and it can do both transcript- and gene-level Differential Expression Analysis (DEA) when transcriptome is used as mapping reference. It requires little programming skill for basic use. If you're good at programming, you can do more magic with RASflow! You can help support RASflow by citing our publication: **Zhang, X., Jonassen, I. RASflow: an RNA-Seq analysis workflow with Snakemake. BMC Bioinformatics 21, 110 (2020). https://doi.org/10.1186/s12859-020-3433-x**
keywords: Transcriptomics
license: CC-BY-4.0
producer: Bioinformatics workflows for life science
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

Mobilome Annotation Pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/27
creator: Alejandra Escobar
date created: 2025-06-17T14:20:31Z
date modified: 2025-06-17T14:24:04Z
description: # Mobilome Annotation Pipeline (former MoMofy) Bacteria can acquire genetic material through horizontal gene transfer, allowing them to rapidly adapt to changing environmental conditions. These mobile genetic elements can be classified into three main categories: plasmids, phages, and integrative elements. Plasmids are mostly extrachromosmal; phages can be found extrachromosmal or as temperate phages (prophages); whereas integrons are stable inserted in the chromosome. Autonomous elements are those integrative elements capable of excising themselves from the chromosome and reintegrate elsewhere. They can use a transposase (like insertion sequences and transposons) or an integrase/excisionase (like ICEs and IMEs). The Mobilome Annotation Pipeline is a wrapper that integrates the output of different tools designed for the prediction of plasmids, phages, insertion sequences, and other autonomous integrative mobile genetic elements such as ICEs, IMEs and integrons in prokaryotic genomes and metagenomes. The output is a PROKKA gff file with extra entries for the mobilome. ## Contents - [ Workflow ](#wf) - [ Setup ](#sp) - [ Install and dependencies ](#install) - [ Usage ](#usage) - [ Inputs ](#in) - [ Outputs ](#out) - [ Tests ](#test) - [ Citation ](#cite) ## Workflow This workflow has the following main subworkflows: - Preprocessing: Rename and filter contigs, and run PROKKA annotation - Prediction: Run geNomad, ICEfinder, IntegronFinder, and ISEScan - Annotation: Generate extra-annotation for antimicrobial resistance genes (AMRFinderPlus) and other mobilome-related proteins (MobileOG). - Integration: Parse and integrate the outputs generated on `Prediction` and `Annotation` subworkflows. In this step optional results of VIRify v3.0.0 can be incorporated. MGEs <500 bp lengh and predictions with no genes are discarded. - Postprocessing: Write the mobilome fasta file, write a report of the location of AMR genes (either mobilome or chromosome), and generate three new GFF …
is based on: https://workflowhub.eu/workflows/452
keywords: Mobilome, Genomics, Metagenomics, Nextflow, MGE
license: Apache-2.0
producer: https://workflowhub.eu/projects/9
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 2

Nextflow
type: ComputerLanguage

BatchConvert
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Bugra Oezdemir
date created: 2023-07-05T13:47:29Z
date modified: 2023-07-11T10:35:35Z
description: # BatchConvert ![DOI:10.5281](https://zenodo.org/badge/doi/10.5281/zenodo.7955974.svg) A command line tool for converting image data into either of the standard file formats OME-TIFF or OME-Zarr. The tool wraps the dedicated file converters bfconvert and bioformats2raw to convert into OME-TIFF or OME-Zarr, respectively. The workflow management system NextFlow is used to perform conversion in parallel for batches of images. The tool also wraps s3 and Aspera clients (go-mc and aspera-cli, respectively). Therefore, input and output locations can be specified as local or remote storage and file transfer will be performed automatically. The conversion can be run on HPC with Slurm. ![](figures/diagram.png) ## Installation & Dependencies **Important** note: The package has been so far only tested on Ubuntu 20.04. The minimal dependency to run the tool is NextFlow, which should be installed and made accessible from the command line. If conda exists on your system, you can install BatchConvert together with NextFlow using the following script: ``` git clone https://github.com/Euro-BioImaging/BatchConvert.git && \ source BatchConvert/installation/install_with_nextflow.sh ``` If you already have NextFlow installed and accessible from the command line (or if you prefer to install it manually e.g., as shown [here](https://www.nextflow.io/docs/latest/getstarted.html)), you can also install BatchConvert alone, using the following script: ``` git clone https://github.com/Euro-BioImaging/BatchConvert.git && \ source BatchConvert/installation/install.sh ``` Other dependencies (which will be **automatically** installed): - bioformats2raw (entrypoint bioformats2raw) - bftools (entrypoint bfconvert) - go-mc (entrypoint mc) - aspera-cli (entrypoint ascp) These dependencies will be pulled and cached automatically at the first execution of the conversion command. The mode of dependency management can be specified by using the command line option ``--profile`` or `-pf`. Depending on how thi…
is based on: https://workflowhub.eu/workflows/453
keywords: Nextflow, bash, Python, NGFF, OME-Zarr, Conversion, imaging, bioimaging, image file format, file conversion, OME-TIFF, S3, BioStudies, bioformats, bioformats2raw
license: MIT
producer: NGFF Tools
producer: https://workflowhub.eu/projects/167
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 3

Nextflow
type: ComputerLanguage

MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/354
creator: Andrey Prjibelski
creator: Anton Korobeynikov
date created: 2023-04-12T09:18:18Z
date modified: 2023-04-13T06:50:58Z
description: **Assembly and quantification metatranscriptome using metagenome data**. Version: see VERSION ## Introduction **MetaGT** is a bioinformatics analysis pipeline used for improving and quantification metatranscriptome assembly using metagenome data. The pipeline supports Illumina sequencing data and complete metagenome and metatranscriptome assemblies. The pipeline involves the alignment of metatranscriprome assembly to the metagenome assembly with further extracting CDSs, which are covered by transcripts. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. [![Nextflow](https://img.shields.io/badge/nextflow-%E2%89%A520.04.0-brightgreen.svg)](https://www.nextflow.io/) [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg)](https://bioconda.github.io/) ## Quick Start 1. Install [`nextflow`](https://nf-co.re/usage/installation) 2. Install any of [`Conda`](https://conda.io/miniconda.html) for full pipeline reproducibility 3. Download the pipeline, e.g. by cloning metaGT GitHub repository: ```bash git clone git@github.com:ablab/metaGT.git ``` 4. Test it on a minimal dataset by running: ```bash nextflow run metaGT -profile test,conda ``` 5. Start running your own analysis! > Typical command for analysis using reads: ```bash nextflow run metaGT -profile --dna_reads '*_R{1,2}.fastq.gz' --rna_reads '*_R{1,2}.fastq.gz' ``` > Typical command for analysis using multiple files with reads: ```bash nextflow run metaGT -profile --dna_reads '*.yaml' --rna_reads '*.yaml' --yaml ``` > Typical command for analysis using assemblies: ```bash nextflow run metaGT -profile --genome '*.fasta' --transcriptome '*.fasta' ``` ## Pipeli…
keywords: Metagenomics, metatranscriptomics, expression, Multi-omics
license: MIT
producer: https://workflowhub.eu/projects/153
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

API key for CDS service
type: FormalParameter
conforms to: 1.0-RELEASE

Longitude for right-edge of domain
type: FormalParameter
conforms to: 1.0-RELEASE

emepcores
type: FormalParameter
conforms to: 1.0-RELEASE

Day for end date
type: FormalParameter
conforms to: 1.0-RELEASE

Month for end date
type: FormalParameter
conforms to: 1.0-RELEASE

Year for end date
type: FormalParameter
conforms to: 1.0-RELEASE

generate_metdir
type: FormalParameter
conforms to: 1.0-RELEASE

generate_rundir
type: FormalParameter
conforms to: 1.0-RELEASE

Geographic inputs for geogrid
type: FormalParameter
conforms to: 1.0-RELEASE

Geogrid data table
type: FormalParameter
conforms to: 1.0-RELEASE

HTTPS proxy information, if needed
type: FormalParameter
conforms to: 1.0-RELEASE

EMEP Input Files
type: FormalParameter
conforms to: 1.0-RELEASE

Directory name for WRF input Files, should match 'meteo' base-directory in namelist
type: FormalParameter
conforms to: 1.0-RELEASE

EMEP configuration file
type: FormalParameter
conforms to: 1.0-RELEASE

Geogrid namelist
type: FormalParameter
conforms to: 1.0-RELEASE

metgrid configuration
type: FormalParameter
conforms to: 1.0-RELEASE

Real preprocessor Configuration File
type: FormalParameter
conforms to: 1.0-RELEASE

Configuration File
type: FormalParameter
conforms to: 1.0-RELEASE

Configuration File
type: FormalParameter
conforms to: 1.0-RELEASE

WRF Configuration File
type: FormalParameter
conforms to: 1.0-RELEASE

Latitude for top of domain
type: FormalParameter
conforms to: 1.0-RELEASE

outname_atm
type: FormalParameter
conforms to: 1.0-RELEASE

outname_sfc
type: FormalParameter
conforms to: 1.0-RELEASE

realcores
type: FormalParameter
conforms to: 1.0-RELEASE

EMEP run label, for output files, should match 'runlabel1' in namelist
type: FormalParameter
conforms to: 1.0-RELEASE

Latitude for bottom of domain
type: FormalParameter
conforms to: 1.0-RELEASE

Day for starting date
type: FormalParameter
conforms to: 1.0-RELEASE

Month for starting date
type: FormalParameter
conforms to: 1.0-RELEASE

Year for starting date
type: FormalParameter
conforms to: 1.0-RELEASE

grib variable table
type: FormalParameter
conforms to: 1.0-RELEASE

grib variable table
type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

output files
type: FormalParameter
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output files
type: FormalParameter
conforms to: 1.0-RELEASE

WRF / EMEP Linear Workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/37
date created: 2023-04-12T12:04:44Z
date modified: 2023-04-12T12:45:16Z
description: # WRF/EMEP Linear Workflow Example Common Workflow Language (CWL) workflow and tool descriptors for running the Weather Research and Forecase (WRF) and EMEP models. This workflow is designed for a single model domain. Example datasets for testing this workflow can be downloaded from Zenodo. ## Requirements: * docker or singularity * conda * cwltool * Toil - optional, useful for running on HPC or distributed computing systems ### CWL / Toil Installation: The workflow runner (either cwltool, or Toil) can be installed using either conda or pip. Environment files for conda are included, and can be used as shown below: * cwltool only: * `conda env create --file install/env_cwlrunner.yml --name cwl` * Toil & cwltool: * `conda env create --file install/env_toil.yml --name toil` ### Setup for Example Workflow * Download the example dataset from Zenodo: https://doi.org/10.5281/zenodo.7817216 * Extract into the `input_files` directory: * `tar -zxvf wrf_emep_UK_example_inputs.tar.gz -C input_files --strip-components=1` ## Running the Workflow The full workflow is broken into several logical steps: 1. ERA5 download 2. WPS 1st step: Geogrid geography file creation 3. WPS process: ungribbing of ERA5 data, and running of metgrid to produce meteorology files. 4. WRF process: generation of WRF input files by REAL, and running of WRF model 5. EMEP model: running of EMEP chemistry and transport model Steps 1 and 3 require you to register with the CDS service, in order to download ERA5 data before using in the WPS process. Steps 2 and 5 require you to download extra input data - the instructions on how to do this are included in the README.txt files in the relevant input data directories. A full workflow for all steps is provided here. But each separate step can by run on it's own too, following the instructions given below. We recommend running step 4 first, to explore how the REAL & WRF workflow works, before trying the other steps. ### 1. ERA5 download. Before running the ERA5 downl…
input: API key for CDS service
input: Longitude for right-edge of domain
input: emepcores
input: Day for end date
input: Month for end date
input: Year for end date
input: generate_metdir
input: generate_rundir
input: Geographic inputs for geogrid
input: Geogrid data table
input: HTTPS proxy information, if needed
input: EMEP Input Files
input: Directory name for WRF input Files, should match 'meteo' base-directory in namelist
input: EMEP configuration file
input: Geogrid namelist
input: metgrid configuration
input: Real preprocessor Configuration File
input: Configuration File
input: WRF Configuration File
input: Latitude for top of domain
input: outname_atm
input: outname_sfc
input: realcores
input: EMEP run label, for output files, should match 'runlabel1' in namelist
input: Latitude for bottom of domain
input: Day for starting date
input: Month for starting date
input: Year for starting date
input: grib variable table
input: Longitude for left-edge of domain
input: wrfcores
license: MIT
output: output files
producer: https://workflowhub.eu/projects/103
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 1

Python
type: ComputerLanguage

Formula
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-04-14T01:48:47Z
date modified: 2023-04-14T01:50:33Z
description: cccccc
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/157
programming language: Python
sd publisher: about.workflowhub.eu
version: 1

Pi
type: ComputerLanguage

PyUtils
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-04-14T02:26:04Z
date modified: 2023-04-14T02:26:04Z
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/157
programming language: Pi
sd publisher: about.workflowhub.eu
version: 1

NGTax
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/46
creator: https://workflowhub.eu/people/84
creator: 45#
creator: Jesse van Dam
creator: Peter Schaap
date created: 2020-10-28T11:07:26Z
date modified: 2023-01-16T13:43:45Z
description: Amplicon analysis workflow using NG-Tax **Steps:** * Quality control on the reads * Execute NGTax for ASV detection and classification For more information about NG-Tax 2.0 have a look at https://doi.org/10.3389/fgene.2019.01366
input: Reverse read length
input: Forward primer
input: forward reads
input: Reference database
input: Reverse primer
input: reverse reads
input: Sample name
is based on: https://workflowhub.eu/workflows/45
keywords: Amplicon, 16S, ITS
license: CC0-1.0
output: files_to_folder_fastqc
output: files_to_folder_ngtax
producer: https://workflowhub.eu/projects/16
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 7

Galaxy
type: ComputerLanguage

Jupyter
type: ComputerLanguage

Gene similariy anaylsis across physiological systems in IMPC phenotype data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/191
creator: https://workflowhub.eu/people/463
date created: 2023-04-27T13:43:13Z
date modified: 2023-06-16T06:10:32Z
description: # Gene similariy anaylsis across physiological systems in IMPC phenotype data A Jupyter Notebook tool for analysing user specified genes across the different physiological systems in IMPC data. **_Input_** The tool takes as input a list of gene ids (MGI ids or Gene Symbol ids). The elemnts in the list could be separated by a comma, semicolumn, tab or newline. **_Operation_** The program will create an heatmap representing the number of phenotypes and the mp term list for each gene contained in an [IMPC physiological system](https://www.mousephenotype.org/help/data-visualization/gene-pages/phenogrid/). Using the slider, adjust the treshold to set the minimum count to be displayed in the heatmap. NB: Genes without phenotypes in any physiological system will not be displayed. Also, the labels of the heatmap will use Gene Symbols independently from the type of id used in the input. **_Tool access:_** [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AndreaFurlani/Jupyter_interactive_plots/main?urlpath=voila%2Frender%2FInteractive_plots.ipynb)
keywords: jupyter
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/68
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 1

Jupyter
type: ComputerLanguage

Mouse phenotype similarity analyis
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/191
creator: https://workflowhub.eu/people/463
date created: 2023-04-27T13:57:37Z
date modified: 2023-05-25T09:07:40Z
description: # **Phenotype similarity analysis** A Jupyter Notebook for analyzing phenotyping similarities across user specified genes. Phenotypes are retrieved from the MGI resource **_Input_** The tool takes as input a list of gene ids (MGI ids or Gene Symbol ids). The elemnts in the list could be separated by a comma, semicolumn, tab or newline. **_Operation_** The Notebook will create a table where row and columns names are the Gene Symbols of the input elements and each cell will contain the name of the common phenotypes shared by those genes. Then an interactive heatmap will be displayed, showing also the count of those phenotypes. Using the slider, adjust the treshold to set the minimum count to be displayed in the heatmap. NB: Genes with only counts below the treshold will be not displayed in the heatmap. Also, the labels of the heatmap will use Gene Symbols independently from the type of id used in the input. **_Tool access:_** [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/AndreaFurlani/Jupyter_alliance/main?urlpath=voila%2Frender%2FAlliance_API_query.ipynb)
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/68
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 1

Nextflow
type: ComputerLanguage

MGnify genomes catalogue pipeline
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/27
creator: https://workflowhub.eu/people/355
creator: Tatiana Gurbich
date created: 2024-12-03T15:41:02Z
date modified: 2024-12-03T15:41:02Z
description: # MGnify genomes catalogue pipeline [MGnify](https://www.ebi.ac.uk/metagenomics/) A pipeline to perform taxonomic and functional annotation and to generate a catalogue from a set of isolate and/or metagenome-assembled genomes (MAGs) using the workflow described in the following publication: Gurbich TA, Almeida A, Beracochea M, Burdett T, Burgin J, Cochrane G, Raj S, Richardson L, Rogers AB, Sakharova E, Salazar GA and Finn RD. (2023) [MGnify Genomes: A Resource for Biome-specific Microbial Genome Catalogues.](https://www.sciencedirect.com/science/article/pii/S0022283623000724) J Mol Biol. doi: https://doi.org/10.1016/j.jmb.2023.168016 Detailed information about existing MGnify catalogues: https://docs.mgnify.org/src/docs/genome-viewer.html ### Tools used in the pipeline | Tool/Database | Version | Purpose | |--------------------------------------------------------------------------------------------------|-------------------|------------------------------------------------------------------------------------------------------------------------| | CheckM2 | 1.0.1 | Determining genome quality | | dRep | 3.2.2 | Genome clustering | | Mash | 2.3 | Sketch for the catalogue; placement of genomes into clusters (update only); strain tree | | GUNC | 1.0.3 | Quality control | | GUNC DB | 2.0.4 | Database for GUNC | | GTDB-Tk | 2.4.0 | Assigning taxonomy; generating alignments | | GTDB | r220 | Database for GTDB-Tk | | Prokka | 1.14.6 | Protein annotation | | IQ-TREE 2 | 2.2.0.3 | Generating a phylogenetic tree | | Kraken 2 | 2.1.2 | Generating a kraken database | | Bracken | 2.6.2 | Generating a bracken database | | MMseqs2 | 13.45111 | Generating a protein catalogue | | eggNOG-mapper | 2.1.11 | Protein annotation (eggNOG, KEGG, COG, CAZy) | | eggNOG DB | 5.0.2 | Database for eggNOG-mapper | | Diamond | 2.0.11 | Protein annotation (eggNOG) | | InterProScan | 5.62-94.0 | Protein annotation (InterPro, Pfam) | | kegg-pathways-completeness tool | 1.0.5 | Computes KEGG pathway com…
is based on: https://workflowhub.eu/workflows/462
keywords: Metagenomics, Nextflow, Bioinformatics
license: Apache-2.0
producer: https://workflowhub.eu/projects/9
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 3

Katdetectr
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0001-7767-7923
creator: https://orcid.org/0000-0002-7513-6813
date created: 2023-05-02T11:51:45Z
date modified: 2023-05-02T11:58:38Z
description: # Introduction `katdetectr` is an *R* package for the detection, characterization and visualization of localized hypermutated regions, often referred to as *kataegis*. Please see the [Application Note](https://www.biorxiv.org/content/10.1101/2022.07.11.499364v1) (under submission) for additional background, details and performance evaluations of `katdetectr`. The general workflow of `katdetectr` can be summarized as follows: 1. Import of genomic variants; VCF, MAF or VRanges objects. 2. Detection of kataegis foci. 3. Visualization of segmentation and kataegis foci. Please see the [vignette](https://bioconductor.org/packages/release/bioc/vignettes/katdetectr/inst/doc/General_overview.html) for an overview of the workflow in a step-by-step manner on publicly-available datasets which are included within this package. ## Installation Download katdetectr from BioConductor: ```R if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("katdetectr") ```
license: GPL-2.0
producer: https://workflowhub.eu/projects/162
sd publisher: about.workflowhub.eu
version: 1

Mike Thang
type: Person

Sarah Williams
type: Person

Valentine Murigneaux
type: Person

Galaxy
type: ComputerLanguage

scRNAseq Single Sample Processing STARSolo
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Mike Thang
creator: Sarah Williams
creator: Valentine Murigneaux
date created: 2023-12-12T03:15:52Z
date modified: 2024-05-30T05:54:50Z
description: From the R1 and R2 fastq files of a single samples, make a scRNAseq counts matrix, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData Depreciated: use individual workflows insead for multiple samples
is based on: https://workflowhub.eu/workflows/465
is part of: https://workflowhub.eu/collections/9
keywords: scRNAseq
license: MIT
producer: https://workflowhub.eu/projects/41
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 4

Mike Thang
type: Person

Sarah Williams
type: Person

Valentine Murigneaux
type: Person

Galaxy
type: ComputerLanguage

#main/AnnData - Loaded
type: FormalParameter
conforms to: 1.0-RELEASE

#main/MaxMTpc
type: FormalParameter
conforms to: 1.0-RELEASE

#main/MinCountPerCell
type: FormalParameter
conforms to: 1.0-RELEASE

#main/MinGenesPerCell
type: FormalParameter
conforms to: 1.0-RELEASE

#main/Mitochondrial Prefix
type: FormalParameter
conforms to: 1.0-RELEASE

#main/genecount_qc_plot
type: FormalParameter
conforms to: 1.0-RELEASE

#main/mito_qc_plot
type: FormalParameter
conforms to: 1.0-RELEASE

#main/qc_anndata_object
type: FormalParameter
conforms to: 1.0-RELEASE

#main/top_genes_plot
type: FormalParameter
conforms to: 1.0-RELEASE

scRNAseq_CellQC
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Mike Thang
creator: Sarah Williams
creator: Valentine Murigneaux
date created: 2024-05-30T05:51:13Z
date modified: 2024-05-30T05:51:13Z
description: Take an anndata file, and perform basic QC with scanpy. Produces a filtered AnnData object.
input: #main/AnnData - Loaded
input: #main/MaxMTpc
input: #main/MinCountPerCell
input: #main/MinGenesPerCell
input: #main/Mitochondrial Prefix
is based on: https://workflowhub.eu/workflows/467
is part of: https://workflowhub.eu/collections/9
keywords: scRNAseq
license: MIT
output: #main/genecount_qc_plot
output: #main/mito_qc_plot
output: #main/qc_anndata_object
output: #main/top_genes_plot
producer: https://workflowhub.eu/projects/41
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 4

Mike Thang
type: Person

Sarah Williams
type: Person

Valentine Murigneaux
type: Person

Galaxy
type: ComputerLanguage

#main/QCFilteredAnnDataObject
type: FormalParameter
conforms to: 1.0-RELEASE

#main/1k_cell_table
type: FormalParameter
conforms to: 1.0-RELEASE

#main/1k_gene_table
type: FormalParameter
conforms to: 1.0-RELEASE

#main/Scanpy FindCluster on input dataset(s): Clusters AnnData
type: FormalParameter
conforms to: 1.0-RELEASE

#main/Scanpy RunUMAP on input dataset(s): UMAP object AnnData
type: FormalParameter
conforms to: 1.0-RELEASE

#main/_anonymous_output_1
type: FormalParameter
conforms to: 1.0-RELEASE

#main/_anonymous_output_2
type: FormalParameter
conforms to: 1.0-RELEASE

#main/_anonymous_output_3
type: FormalParameter
conforms to: 1.0-RELEASE

#main/_anonymous_output_4
type: FormalParameter
conforms to: 1.0-RELEASE

#main/_anonymous_output_5
type: FormalParameter
conforms to: 1.0-RELEASE

#main/marker_dot_plot
type: FormalParameter
conforms to: 1.0-RELEASE

#main/marker_table
type: FormalParameter
conforms to: 1.0-RELEASE

#main/processed_anndata_object
type: FormalParameter
conforms to: 1.0-RELEASE

#main/umap_cluster_plot
type: FormalParameter
conforms to: 1.0-RELEASE

#main/umap_sample_plot
type: FormalParameter
conforms to: 1.0-RELEASE

scRNAseq_QCtoBasicProcessing
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Mike Thang
creator: Sarah Williams
creator: Valentine Murigneaux
date created: 2024-05-30T05:52:35Z
date modified: 2024-05-30T05:52:35Z
description: Basic processing of a QC-filtered Anndata Object. UMAP, clustering e.t.c
input: #main/QCFilteredAnnDataObject
is based on: https://workflowhub.eu/workflows/468
is part of: https://workflowhub.eu/collections/9
keywords: scRNAseq
license: MIT
output: #main/1k_cell_table
output: #main/1k_gene_table
output: #main/Scanpy FindCluster on input dataset(s): Clusters AnnData
output: #main/Scanpy RunUMAP on input dataset(s): UMAP object AnnData
output: #main/_anonymous_output_1
output: #main/_anonymous_output_2
output: #main/_anonymous_output_3
output: #main/_anonymous_output_4
output: #main/_anonymous_output_5
output: #main/marker_dot_plot
output: #main/marker_table
output: #main/processed_anndata_object
output: #main/umap_cluster_plot
output: #main/umap_sample_plot
producer: https://workflowhub.eu/projects/41
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 3

Rbbt
type: ComputerLanguage
alternate name: Ruby bioinformatics toolkit

PerMedCoE Covid19 Pilot workflow (Rbbt)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-05-09T09:43:39Z
date modified: 2023-05-23T12:33:53Z
description: Rbbt implementation of the Covid-19 pilot workflow from the Personalized Medicine Center of Excellence. This workflow processes single cell data to personalize boolean models that are then used in a multi-scale cellular simulation using PhysiBoSS.
license: MIT
producer: https://workflowhub.eu/projects/165
programming language: Rbbt
sd publisher: about.workflowhub.eu
version: 1

Copernicus Essential Climate Variable - select and plot
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-07-23T18:22:21Z
date modified: 2023-01-16T13:44:09Z
description: Abstract CWL Automatically generated from the Galaxy workflow file: Workflow with Copernicus Essential Climate Variable - select and plot
keywords: Galaxy, Climate, copernicus
license: Apache-2.0
producer: https://workflowhub.eu/projects/18
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Jupyter
type: ComputerLanguage

Galaxy
type: ComputerLanguage

_anonymous_output_1
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_10
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_11
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_12
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_13
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_14
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_15
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_16
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_17
type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

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type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_7
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_8
type: FormalParameter
conforms to: 1.0-RELEASE

_anonymous_output_9
type: FormalParameter
conforms to: 1.0-RELEASE

Workflow 4: Staramr
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-05-11T08:29:41Z
date modified: 2024-09-09T08:06:05Z
description: Correlation between Phenotypic and In Silico Detection of Antimicrobial Resistance in Salmonella enterica in Canada Using Staramr. Doi: [10.3390/microorganisms10020292](https://doi.org/10.3390/microorganisms10020292) | tool | version | license | | -- | -- | -- | | staramr | 0.8.0 | [Apache-2.0 license](https://github.com/phac-nml/staramr/blob/development/LICENSE) |
keywords: AMR, AMR-detection, 10.3390/microorganisms10020292, Bioinformatics, antimicrobial resistance
license: GPL-3.0
output: _anonymous_output_1
output: _anonymous_output_10
output: _anonymous_output_11
output: _anonymous_output_12
output: _anonymous_output_13
output: _anonymous_output_14
output: _anonymous_output_15
output: _anonymous_output_16
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output: _anonymous_output_18
output: _anonymous_output_19
output: _anonymous_output_2
output: _anonymous_output_3
output: _anonymous_output_4
output: _anonymous_output_5
output: _anonymous_output_6
output: _anonymous_output_7
output: _anonymous_output_8
output: _anonymous_output_9
producer: https://workflowhub.eu/projects/110
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

body
type: FormalParameter
conforms to: 1.0-RELEASE

is_availability
type: FormalParameter
conforms to: 1.0-RELEASE

result_modifiers
type: FormalParameter
conforms to: 1.0-RELEASE

results
type: FormalParameter
conforms to: 1.0-RELEASE

output_file
type: FormalParameter
conforms to: 1.0-RELEASE

rquest-omop-worker-workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Vasiliki Panagi
date created: 2023-10-23T13:58:11Z
date modified: 2024-11-12T12:55:51Z
description: # rquest-omop-worker-workflows Source for workflow definitions for the open source RQuest OMOP Worker tool developed for Hutch/TRE-FX Note: ARM workflows are currently broken. x86 ones work. ## Inputs ### Body Sample input payload: ```json { "task_id": "job-2023-01-13-14: 20: 38-", "project": "", "owner": "", "cohort": { "groups": [ { "rules": [ { "varname": "OMOP", "varcat": "Person", "type": "TEXT", "oper": "=", "value": "8507" } ], "rules_oper": "AND" } ], "groups_oper": "OR" }, "collection": "", "protocol_version": "", "char_salt": "", "uuid": "" } ``` ### Database access Currently this workflow requires inputs for connecting to the database it will run queries against. In future this may be moved to environment variables.
input: body
input: is_availability
input: result_modifiers
input: results
is based on: https://workflowhub.eu/workflows/471
license: MIT
output: output_file
producer: TRE-FX
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 3

Galaxy
type: ComputerLanguage

CPAT_header_tab
type: FormalParameter
conforms to: 1.0-RELEASE

GRCh38_p13_genome_fa_gz
type: FormalParameter
conforms to: 1.0-RELEASE

Input Dataset Collection
type: FormalParameter
conforms to: 1.0-RELEASE

Pfam-A_hmm_dat_gz
type: FormalParameter
conforms to: 1.0-RELEASE

Pfam-A_hmm_gz
type: FormalParameter
conforms to: 1.0-RELEASE

active_site_dat_gz
type: FormalParameter
conforms to: 1.0-RELEASE

gencode_v43_annotation_gtf_gz
type: FormalParameter
conforms to: 1.0-RELEASE

gencode_v43_lncRNA_transcripts_fa_gz
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conforms to: 1.0-RELEASE

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conforms to: 1.0-RELEASE

gencode_v43_transcripts_fa_gz
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conforms to: 1.0-RELEASE

Genome-wide alternative splicing analysis
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/247
date created: 2023-05-25T21:57:53Z
date modified: 2023-05-25T21:58:58Z
description: This workflow correspond to the Genome-wide alternative splicing analysis training. It allows to analyze isoform switching by making use of IsoformSwitchAnalyzeR.
input: CPAT_header_tab
input: GRCh38_p13_genome_fa_gz
input: Input Dataset Collection
input: Pfam-A_hmm_dat_gz
input: Pfam-A_hmm_gz
input: active_site_dat_gz
input: gencode_v43_annotation_gtf_gz
input: gencode_v43_lncRNA_transcripts_fa_gz
input: gencode_v43_pc_transcripts_fa_gz
input: gencode_v43_transcripts_fa_gz
is based on: https://workflowhub.eu/workflows/472
keywords: Transcriptomics, alternative-splicing, GTN
license: CC-BY-NC-4.0
producer: https://workflowhub.eu/projects/12
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 4

Uniform Interface to Computing Resources
type: ComputerLanguage
alternate name: UNICORE

Sorting and registration of Terahertz ELBE raw data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-05-17T12:08:56Z
date modified: 2023-06-07T13:15:20Z
description: The radiation source ELBE (Electron Linac for beams with high Brilliance and low Emittance) at the Helmholtz Centre Dresden Rossendorf (HZDR) can produce several kinds of secondary radiations. THz radiation is one of them and can be used with a typical pulse frequency of 100 kHz as a stimulation source for elementary low-energy degrees of freedom in matter. To sample the whole THz wave the laser path length is modified by moving specific mirrors. The raw data contains for each mirror position a binary file storing the signal spectra and a folder with gray scaled tiff files storing the jitter timing. This Workflow is equivalent to the first part of the standalone jupyter notebook https://github.com/hzdr/TELBE-raw-data-evaluation/blob/main/sorting_binning.ipynb In the job file the folder < FOLDER_BASE> and < FOLDER_SUB> needs to be specified and the parameters as a json string like < PARAMS> = { "rep": 100000, "t_exp": 1, "N_sample": 96, "offset": 0, "pixel_to_ps": 0.0115, "Stage_zero": 0 } The python file which is used is originally published in gitlab https://codebase.helmholtz.cloud/science2workflow/telbe-sorting-binning/-/blob/master/src/ The workflow can automatically be monitored in Heliport if the project number < HELIPORT_PROJECT> is provided.
license: CC-BY-4.0
producer: Helmholtz Scientific Project Workflow Platform
programming language: Uniform Interface to Computing Resources
sd publisher: about.workflowhub.eu
version: 1

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

Metagenome and metatranscriptome assembly in CWL
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-06-21T10:41:38Z
date modified: 2023-06-21T10:41:38Z
description: # CWL-assembly [![Codacy Badge](https://api.codacy.com/project/badge/Grade/684724bbc0134960ab41748f4a4b732f)](https://www.codacy.com/app/mb1069/CWL-assembly?utm_source=github.com&utm_medium=referral&utm_content=EBI-Metagenomics/CWL-assembly&utm_campaign=Badge_Grade) [![Build Status](https://travis-ci.org/EBI-Metagenomics/CWL-assembly.svg?branch=develop)](https://travis-ci.org/EBI-Metagenomics/CWL-assembly) ## Description This repository contains two workflows for metagenome and metatranscriptome assembly of short read data. MetaSPAdes is used as default for paired-end data, and MEGAHIT for single-end data and co-assemblies. MEGAHIT can be specified as the default assembler in the yaml file if preferred. Steps include: * _QC_: removal of short reads, low quality regions, adapters and host decontamination * _Assembly_: with metaSPADES or MEGAHIT * _Post-assembly_: Host and PhiX decontamination, contig length filter (500bp), stats generation ## Requirements - How to install This pipeline requires a conda environment with cwltool, blastn, and metaspades. If created with `requirements.yml`, the environment will be called `cwl_assembly`. ``` conda env create -f requirements.yml conda activate cwl_assembly pip install cwltool==3.1.20230601100705 ``` ## Databases You will need to pre-download fasta files for host decontamination and generate the following databases accordingly: * bwa index * blast index Specify the locations in the yaml file when running the pipeline. ## Main pipeline executables * `src/workflows/metagenome_pipeline.cwl` * `src/workflows/metatranscriptome_pipeline.cwl` ## Example command ```cwltool --singularity --outdir ${OUTDIR} ${CWL} ${YML}``` `$CWL` is going to be one of the executables mentioned above `$YML` should be a config yaml file including entries among what follows. You can find a yml template in the `examples` folder. ## Example output directory structure ``` Root directory ├── megahit │ └── 001 -------------------------------- Assembly root …
is based on: https://workflowhub.eu/workflows/474
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/153
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 2

Snakemake
type: ComputerLanguage

EukRecover
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-05-19T14:02:24Z
date modified: 2023-05-19T14:02:24Z
description: # EukRecover Pipeline to recover eukaryotic MAGs using CONCOCT, metaBAT2 and EukCC's merging algorythm. Needs paired end shotgun metagenomic reads. ## Environment Eukrecover requires an environment with snakemake and metaWRAP. ## Quickstart Define your samples in the file `samples.csv`. This file needs to have the columns project and run to identify each metagenome. This pipeline does not support co-binning, but feel free to change it. Clone this repro wherever you want to run the pipeline: ``` git clone https://github.com/openpaul/eukrecover/ ``` You can then run the snakemake like so ``` snakemake --use-singularity ``` The pipeline used dockerhub to fetch all tools, so make sure you have singularity installed. ## Prepare databases The pipeline will setup databases for you, but if you already have a EukCC or a BUSCO 5 database you can use them by specifying the location in the file `config/config.yaml` ## Output: In the folder results you will find a folder `MAGs` which will contain a folder `fa` containing the actual MAG fastas. In addition you will find stats for each MAG in the table `QC.csv`. This table contains the following columns: name,eukcc_compl,eukcc_cont,BUSCO_C,BUSCO_M,BUSCO_D,BUSCO_F,BUSCO_tax,N50,bp ## Citation: If you use this pipeline please make sure to cite all used software. For this please reffer to the used rules.
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/153
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

COMPSs
type: ComputerLanguage

PerMedCoE Covid19 Pilot workflow (PyCOMPSs)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-05-23T12:07:56Z
date modified: 2023-05-23T12:33:23Z
description: # COVID-19 Multiscale Modelling of the Virus and Patients’ Tissue Workflow ## Table of Contents - [COVID-19 Multiscale Modelling of the Virus and Patients’ Tissue Workflow](#covid-19-multiscale-modelling-of-the-virus-and-patients-tissue-workflow) - [Table of Contents](#table-of-contents) - [Description](#description) - [Contents](#contents) - [Building Blocks](#building-blocks) - [Workflows](#workflows) - [Resources](#resources) - [Tests](#tests) - [Instructions](#instructions) - [Local machine](#local-machine) - [Requirements](#requirements) - [Usage steps](#usage-steps) - [MareNostrum 4](#marenostrum-4) - [Requirements in MN4](#requirements-in-mn4) - [Usage steps in MN4](#usage-steps-in-mn4) - [Mahti or Puhti](#mahti-or-puhti) - [Requirements](#requirements) - [Steps](#steps) - [License](#license) - [Contact](#contact) ## Description Uses multiscale simulations to predict patient-specific SARS‑CoV‑2 severity subtypes (moderate, severe or control), using single-cell RNA-Seq data, MaBoSS and PhysiBoSS. Boolean models are used to determine the behaviour of individual agents as a function of extracellular conditions and the concentration of different substrates, including the number of virions. Predictions of severity subtypes are based on a meta-analysis of personalised model outputs simulating cellular apoptosis regulation in epithelial cells infected by SARS‑CoV‑2. The workflow uses the following building blocks, described in order of execution: 1. High-throughput mutant analysis 2. Single-cell processing 3. Personalise patient 4. PhysiBoSS 5. Analysis of all simulations For details on individual workflow steps, see the user documentation for each building block. [`GitHub repository`]() ## Contents ### Building Blocks The ``BuildingBlocks`` folder contains the script to install the Building Blocks used in the COVID-19 Workflow. ### Workflows The ``Workflow`` folder contains the workflows implementations. Currently contains the implementation using PyCOMPSs and Snak…
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/165
programming language: COMPSs
sd publisher: about.workflowhub.eu
version: 1

COMPSs
type: ComputerLanguage

PerMedCoE Cancer Invasion
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-05-23T12:10:39Z
date modified: 2023-05-23T12:34:23Z
description: # Cancer Invasion Workflow ## Table of Contents - [Cancer Invasion Workflow](#cancer-invasion-workflow) - [Table of Contents](#table-of-contents) - [Description](#description) - [Contents](#contents) - [Building Blocks](#building-blocks) - [Workflows](#workflows) - [Resources](#resources) - [Tests](#tests) - [Instructions](#instructions) - [Local machine](#local-machine) - [Requirements](#requirements) - [Usage steps](#usage-steps) - [MareNostrum 4](#marenostrum-4) - [Requirements in MN4](#requirements-in-mn4) - [Usage steps in MN4](#usage-steps-in-mn4) - [Mahti or Puhti](#mahti-or-puhti) - [Requirements](#requirements) - [Steps](#steps) - [License](#license) - [Contact](#contact) ## Description Uses multiscale simulations to describe cancer progression into invasion. The workflow uses the following building blocks, described in order of execution: 1. PhysiBoSS-Invasion For details on individual workflow steps, see the user documentation for each building block. [`GitHub repository`]() ## Contents ### Building Blocks The ``BuildingBlocks`` folder contains the script to install the Building Blocks used in the Cancer Invasion Workflow. ### Workflows The ``Workflow`` folder contains the workflows implementations. Currently contains the implementation using PyCOMPSs and Snakemake (in progress). ### Resources The ``Resources`` folder contains dataset files. ### Tests The ``Tests`` folder contains the scripts that run each Building Block used in the workflow for the given small dataset. They can be executed individually for testing purposes. ## Instructions ### Local machine This section explains the requirements and usage for the Cancer Invasion Workflow in a laptop or desktop computer. #### Requirements - [`permedcoe`](https://github.com/PerMedCoE/permedcoe) package - [PyCOMPSs](https://pycompss.readthedocs.io/en/stable/Sections/00_Quickstart.html) / [Snakemake](https://snakemake.readthedocs.io/en/stable/) - [Singularity](https://sylabs.io/guides/3.0/user-guide/installa…
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/165
programming language: COMPSs
sd publisher: about.workflowhub.eu
version: 1

COMPSs
type: ComputerLanguage

PerMedCoE Single Drug Prediction
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-05-23T12:15:44Z
date modified: 2023-05-23T12:32:48Z
description: # Single drug prediction Workflow ## Table of Contents - [Single drug prediction Workflow](#single-drug-prediction-workflow) - [Table of Contents](#table-of-contents) - [Description](#description) - [Contents](#contents) - [Building Blocks](#building-blocks) - [Workflows](#workflows) - [Resources](#resources) - [Tests](#tests) - [Instructions](#instructions) - [Local machine](#local-machine) - [Requirements](#requirements) - [Usage steps](#usage-steps) - [MareNostrum 4](#marenostrum-4) - [Requirements in MN4](#requirements-in-mn4) - [Usage steps in MN4](#usage-steps-in-mn4) - [License](#license) - [Contact](#contact) ## Description Complementarily, the workflow supports single drug response predictions to provide a baseline prediction in cases where drug response information for a given drug and cell line is not available. As an input, the workflow needs basal gene expression data for a cell, the drug targets (they need to be known for untested drugs) and optionally CARNIVAL features (sub-network activity predicted with CARNIVAL building block) and predicts log(IC50) values. This workflow uses a custom matrix factorization approach built with Google JAX and trained with gradient descent. The workflow can be used both for training a model, and for predicting new drug responses. The workflow uses the following building blocks in order of execution (for training a model): 1. Carnival_gex_preprocess - Preprocessed the basal gene expression data from GDSC. The input is a matrix of Gene x Sample expression data. 2. Progeny - Using the preprocessed data, it estimates pathway activities for each column in the data (for each sample). It returns a matrix of Pathways x Samples with activity values for 11 pathways. 3. Omnipath - It downloads latest Prior Knowledge Network of signalling. This building block can be ommited if there exists already a csv file with the network. 4. TF Enrichment - For each sample, transcription factor activities are estimated using Dorothea. 5. Carni…
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/165
programming language: COMPSs
sd publisher: about.workflowhub.eu
version: 1

COMPSs
type: ComputerLanguage

PerMedCoE Drug Synergy
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2023-05-23T12:36:45Z
date modified: 2023-05-23T12:36:45Z
description: # Drug Synergies Screening Workflow ## Table of Contents - [Drug Synergies Screening Workflow](#drug-synergies-screening-workflow) - [Table of Contents](#table-of-contents) - [Description](#description) - [Contents](#contents) - [Building Blocks](#building-blocks) - [Workflows](#workflows) - [Resources](#resources) - [Tests](#tests) - [Instructions](#instructions) - [Local machine](#local-machine) - [Requirements](#requirements) - [Usage steps](#usage-steps) - [MareNostrum 4](#marenostrum-4) - [Requirements in MN4](#requirements-in-mn4) - [Usage steps in MN4](#usage-steps-in-mn4) - [License](#license) - [Contact](#contact) ## Description This pipeline simulates a drug screening on personalised cell line models. It automatically builds Boolean models of interest, then uses cell lines data (expression, mutations, copy number variations) to personalise them as MaBoSS models. Finally, this pipeline simulates multiple drug intervention on these MaBoSS models, and lists drug synergies of interest. The workflow uses the following building blocks, described in order of execution: 1. Build model from species 2. Personalise patient 3. MaBoSS 4. Print drug results For details on individual workflow steps, see the user documentation for each building block. [`GitHub repository`](https://github.com/PerMedCoE/drug-synergies-workflow>) ## Contents ### Building Blocks The ``BuildingBlocks`` folder contains the script to install the Building Blocks used in the Drug Synergies Workflow. ### Workflows The ``Workflow`` folder contains the workflows implementations. Currently contains the implementation using PyCOMPSs. ### Resources The ``Resources`` folder contains a small dataset for testing purposes. ### Tests The ``Tests`` folder contains the scripts that run each Building Block used in the workflow for a small dataset. They can be executed individually *without PyCOMPSs installed* for testing purposes. ## Instructions ### Local machine This section explains the requirements and usag…
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/165
programming language: COMPSs
sd publisher: about.workflowhub.eu
version: 1

RATECE-PLANICA ski station (Slovenia) under CMIP-6 SSP585 condition
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
date created: 2020-07-23T18:33:44Z
date modified: 2023-01-16T13:44:15Z
description: Description: SSP-based RCP scenario with high radiative forcing by the end of century. Following approximately RCP8.5 global forcing pathway with SSP5 socioeconomic conditions. Concentration-driven. Rationale: the scenario represents the high end of plausible future pathways. SSP5 is the only SSP with emissions high enough to produce the 8.5 W/m2 level of forcing in 2100. This workflow is answering to the following scientific question: - Is it worth investing in artificial snowmaking equipment at RATECE-PLANICA?
keywords: Climate, jupyter, cmip6
license: Apache-2.0
producer: https://workflowhub.eu/projects/18
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 1

Henrik Nortamo
type: Person

Nextflow
type: ComputerLanguage

PerMedCoE Covid19 Pilot workflow (Nextflow)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Henrik Nortamo
date created: 2023-05-23T13:23:14Z
date modified: 2023-05-23T13:23:14Z
description: # COVID-19 Multiscale Modelling of the Virus and Patients’ Tissue Workflow ## Table of Contents - [COVID-19 Multiscale Modelling of the Virus and Patients’ Tissue Workflow](#covid-19-multiscale-modelling-of-the-virus-and-patients-tissue-workflow) - [Table of Contents](#table-of-contents) - [Description](#description) - [Contents](#contents) - [Building Blocks](#building-blocks) - [Workflows](#workflows) - [Resources](#resources) - [Tests](#tests) - [Instructions](#instructions) - [Local machine](#local-machine) - [Requirements](#requirements) - [Usage steps](#usage-steps) - [MareNostrum 4](#marenostrum-4) - [Requirements in MN4](#requirements-in-mn4) - [Usage steps in MN4](#usage-steps-in-mn4) - [Mahti or Puhti](#mahti-or-puhti) - [Requirements](#requirements) - [Steps](#steps) - [License](#license) - [Contact](#contact) ## Description Uses multiscale simulations to predict patient-specific SARS‑CoV‑2 severity subtypes (moderate, severe or control), using single-cell RNA-Seq data, MaBoSS and PhysiBoSS. Boolean models are used to determine the behaviour of individual agents as a function of extracellular conditions and the concentration of different substrates, including the number of virions. Predictions of severity subtypes are based on a meta-analysis of personalised model outputs simulating cellular apoptosis regulation in epithelial cells infected by SARS‑CoV‑2. The workflow uses the following building blocks, described in order of execution: 1. High-throughput mutant analysis 2. Single-cell processing 3. Personalise patient 4. PhysiBoSS 5. Analysis of all simulations For details on individual workflow steps, see the user documentation for each building block. [`GitHub repository`]() ## Contents ### Building Blocks The ``BuildingBlocks`` folder contains the script to install the Building Blocks used in the COVID-19 Workflow. ### Workflows The ``Workflow`` folder contains the workflows implementations. Currently contains the implementation using PyCOMPSs and Snak…
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/165
programming language: Nextflow
sd publisher: about.workflowhub.eu
version: 1

Henrik Nortamo
type: Person

Snakemake
type: ComputerLanguage

PerMedCoE Covid19 Pilot workflow (Snakemake)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Henrik Nortamo
date created: 2023-05-23T13:24:53Z
date modified: 2023-05-23T13:24:53Z
description: # COVID-19 Multiscale Modelling of the Virus and Patients’ Tissue Workflow ## Table of Contents - [COVID-19 Multiscale Modelling of the Virus and Patients’ Tissue Workflow](#covid-19-multiscale-modelling-of-the-virus-and-patients-tissue-workflow) - [Table of Contents](#table-of-contents) - [Description](#description) - [Contents](#contents) - [Building Blocks](#building-blocks) - [Workflows](#workflows) - [Resources](#resources) - [Tests](#tests) - [Instructions](#instructions) - [Local machine](#local-machine) - [Requirements](#requirements) - [Usage steps](#usage-steps) - [MareNostrum 4](#marenostrum-4) - [Requirements in MN4](#requirements-in-mn4) - [Usage steps in MN4](#usage-steps-in-mn4) - [Mahti or Puhti](#mahti-or-puhti) - [Requirements](#requirements) - [Steps](#steps) - [License](#license) - [Contact](#contact) ## Description Uses multiscale simulations to predict patient-specific SARS‑CoV‑2 severity subtypes (moderate, severe or control), using single-cell RNA-Seq data, MaBoSS and PhysiBoSS. Boolean models are used to determine the behaviour of individual agents as a function of extracellular conditions and the concentration of different substrates, including the number of virions. Predictions of severity subtypes are based on a meta-analysis of personalised model outputs simulating cellular apoptosis regulation in epithelial cells infected by SARS‑CoV‑2. The workflow uses the following building blocks, described in order of execution: 1. High-throughput mutant analysis 2. Single-cell processing 3. Personalise patient 4. PhysiBoSS 5. Analysis of all simulations For details on individual workflow steps, see the user documentation for each building block. [`GitHub repository`]() ## Contents ### Building Blocks The ``BuildingBlocks`` folder contains the script to install the Building Blocks used in the COVID-19 Workflow. ### Workflows The ``Workflow`` folder contains the workflows implementations. Currently contains the implementation using PyCOMPSs and Snak…
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/165
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 1

Galaxy
type: ComputerLanguage

Active sites dataset
type: FormalParameter
conforms to: 1.0-RELEASE

CPAT header
type: FormalParameter
conforms to: 1.0-RELEASE

Control IDs
type: FormalParameter
conforms to: 1.0-RELEASE

Genome annotation
type: FormalParameter
conforms to: 1.0-RELEASE

Pfam-A HMM Stockholm file
type: FormalParameter
conforms to: 1.0-RELEASE

Pfam-A HMM library
type: FormalParameter
conforms to: 1.0-RELEASE

RNA-seq data collection
type: FormalParameter
conforms to: 1.0-RELEASE

Reference genome
type: FormalParameter
conforms to: 1.0-RELEASE

Genome-wide alternative splicing analysis v.2
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/247
date created: 2023-06-11T19:17:35Z
date modified: 2023-06-11T19:17:56Z
description: Genome-wide alternative splicing analysis v.2
input: Active sites dataset
input: CPAT header
input: Control IDs
input: Genome annotation
input: Pfam-A HMM Stockholm file
input: Pfam-A HMM library
input: RNA-seq data collection
input: Reference genome
is based on: https://workflowhub.eu/workflows/482
keywords: Transcriptomics, Alternative splicing, isoform switching
license: CC-BY-NC-4.0
producer: https://workflowhub.eu/projects/12
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 7

Galaxy
type: ComputerLanguage

Genome annotation
type: FormalParameter
conforms to: 1.0-RELEASE

Reference genome
type: FormalParameter
conforms to: 1.0-RELEASE

StringTie workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/247
date created: 2023-05-25T23:15:06Z
date modified: 2023-05-25T23:15:06Z
description: Abstract CWL Automatically generated from the Galaxy workflow file: Copy of Genome-wide alternative splicing analysis
input: Genome annotation
input: Reference genome
license: CC-BY-NC-4.0
producer: https://workflowhub.eu/projects/12
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

COMPSs
type: ComputerLanguage

Java COMPSs Matrix Multiplication, out-of-core, using files
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing)
creator: https://workflowhub.eu/people/556
date created: 2023-05-30T07:23:13Z
date modified: 2023-10-28T07:59:47Z
description: **Name:** Matrix Multiplication **Contact Person:** support-compss@bsc.es **Access Level:** public **License Agreement:** Apache2 **Platform:** COMPSs # Description Matrix multiplication is a binary operation that takes a pair of matrices and produces another matrix. If A is an n×m matrix and B is an m×p matrix, the result AB of their multiplication is an n×p matrix defined only if the number of columns m in A is equal to the number of rows m in B. When multiplying A and B, the elements of the rows in A are multiplied with corresponding columns in B. In this implementation, A and B are square matrices (same number of rows and columns), and so it is the result matrix C. Each matrix is divided in N blocks of M doubles. The multiplication of two blocks is done by a multiply task method with a simple three-nested-loop implementation. When executed with COMPSs, the main program generates N^3^ tasks arranged as N^2^ chains of N tasks in the dependency graph. # Versions There are three versions of Matrix Multiplication, depending on the data types used to store the blocks. ## Version 1 ''files'', where the matrix blocks are stored in files. ## Version 2 ''objects'', where the matrix blocks are represented by objects. ## Version 3 ''arrays'', where the matrix blocks are stored in arrays. # Execution instructions Usage: ``` runcompss matmul.files.Matmul numberOfBlocks blockSize runcompss matmul.objects.Matmul numberOfBlocks blockSize runcompss matmul.arrays.Matmul numberOfBlocks blockSize ``` where: * numberOfBlocks: Number of blocks inside each matrix * blockSize: Size of each block # Execution Example ``` runcompss matmul.objects.Matmul 16 4 runcompss matmul.files.Matmul 16 4 runcompss matmul.arrays.Matmul 16 4 ``` # Build ## Option 1: Native java ``` cd ~/tutorial_apps/java/matmul/; javac src/main/java/matmul/*/*.java cd src/main/java/; jar cf matmul.jar matmul/ cd ../../../; mv src/main/java/matmul.jar jar/ ``` ## Option 2: Maven ``` cd ~/tutorial_apps/java/matmul/ mvn c…
keywords: Java, COMPSs, Tutorial, Example, Laptop, data_persistence
license: Apache-2.0
producer: https://workflowhub.eu/projects/102
producer: https://workflowhub.eu/projects/172
programming language: COMPSs
sd publisher: about.workflowhub.eu
version: 1

COMPSs
type: ComputerLanguage

PyCOMPSs Matrix Multiplication, out-of-core, using files
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)
creator: https://workflowhub.eu/people/592
date created: 2023-05-30T08:55:07Z
date modified: 2023-10-27T12:19:50Z
description: **Name:** Matrix multiplication with Files **Contact Person**: support-compss@bsc.es **Access Level**: public **License Agreement**: Apache2 **Platform**: COMPSs # Description Matrix multiplication is a binary operation that takes a pair of matrices and produces another matrix. If A is an n×m matrix and B is an m×p matrix, the result AB of their multiplication is an n×p matrix defined only if the number of columns m in A is equal to the number of rows m in B. When multiplying A and B, the elements of the rows in A are multiplied with corresponding columns in B. In this implementation, A and B are square matrices (same number of rows and columns), and so it is the result matrix C. Each matrix is divided in N blocks of M doubles. The multiplication of two blocks is done by a multiply task method with a simple three-nested-loop implementation. When executed with COMPSs, the main program generates N^3^ tasks arranged as N^2^ chains of N tasks in the dependency graph. # Execution instructions Usage: ``` runcompss --lang=python src/matmul_files.py numberOfBlocks blockSize ``` where: * numberOfBlocks: Number of blocks inside each matrix * blockSize: Size of each block # Execution Examples ``` runcompss --lang=python src/matmul_files.py 4 4 runcompss src/matmul_files.py 4 4 python -m pycompss src/matmul_files.py 4 4 ``` # Build No build is required
keywords: PyCOMPSs, Tutorial, Example, Laptop, data_persistence
license: Apache-2.0
producer: https://workflowhub.eu/projects/102
producer: https://workflowhub.eu/projects/172
programming language: COMPSs
sd publisher: about.workflowhub.eu
version: 1

Jupyter
type: ComputerLanguage

Jupyter Notebook Protein conformational ensembles generation
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/47
creator: https://workflowhub.eu/people/57
date created: 2026-03-25T10:18:11Z
date modified: 2026-03-25T10:37:39Z
description: # Protein Conformational ensembles generation ## Workflow included in the [ELIXIR 3D-Bioinfo](https://elixir-europe.org/communities/3d-bioinfo) Implementation Study: ### Building on PDBe-KB to chart and characterize the conformation landscape of native proteins This tutorial aims to illustrate the process of generating **protein conformational ensembles** from** 3D structures **and analysing its **molecular flexibility**, step by step, using the **BioExcel Building Blocks library (biobb)**. ## Conformational landscape of native proteins **Proteins** are **dynamic** systems that adopt multiple **conformational states**, a property essential for many **biological processes** (e.g. binding other proteins, nucleic acids, small molecule ligands, or switching between functionaly active and inactive states). Characterizing the different **conformational states** of proteins and the transitions between them is therefore critical for gaining insight into their **biological function** and can help explain the effects of genetic variants in **health** and **disease** and the action of drugs. **Structural biology** has become increasingly efficient in sampling the different **conformational states** of proteins. The **PDB** has currently archived more than **170,000 individual structures**, but over two thirds of these structures represent **multiple conformations** of the same or related protein, observed in different crystal forms, when interacting with other proteins or other macromolecules, or upon binding small molecule ligands. Charting this conformational diversity across the PDB can therefore be employed to build a useful approximation of the **conformational landscape** of native proteins. A number of resources and **tools** describing and characterizing various often complementary aspects of protein **conformational diversity** in known structures have been developed, notably by groups in Europe. These tools include algorithms with varying degree of sophistication, fo…
is based on: https://workflowhub.eu/workflows/486
is part of: https://workflowhub.eu/collections/19
license: https://workflowhub.eu/workflows/486
producer: https://workflowhub.eu/projects/11
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date created: 2026-03-25T10:31:40Z
date modified: 2026-03-25T10:33:53Z
description: # Protein Conformational ensembles generation ## Workflow included in the [ELIXIR 3D-Bioinfo](https://elixir-europe.org/communities/3d-bioinfo) Implementation Study: ### Building on PDBe-KB to chart and characterize the conformation landscape of native proteins This tutorial aims to illustrate the process of generating **protein conformational ensembles** from** 3D structures **and analysing its **molecular flexibility**, step by step, using the **BioExcel Building Blocks library (biobb)**. ## Conformational landscape of native proteins **Proteins** are **dynamic** systems that adopt multiple **conformational states**, a property essential for many **biological processes** (e.g. binding other proteins, nucleic acids, small molecule ligands, or switching between functionaly active and inactive states). Characterizing the different **conformational states** of proteins and the transitions between them is therefore critical for gaining insight into their **biological function** and can help explain the effects of genetic variants in **health** and **disease** and the action of drugs. **Structural biology** has become increasingly efficient in sampling the different **conformational states** of proteins. The **PDB** has currently archived more than **170,000 individual structures**, but over two thirds of these structures represent **multiple conformations** of the same or related protein, observed in different crystal forms, when interacting with other proteins or other macromolecules, or upon binding small molecule ligands. Charting this conformational diversity across the PDB can therefore be employed to build a useful approximation of the **conformational landscape** of native proteins. A number of resources and **tools** describing and characterizing various often complementary aspects of protein **conformational diversity** in known structures have been developed, notably by groups in Europe. These tools include algorithms with varying degree of sophistication, fo…
is based on: https://workflowhub.eu/workflows/487
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CWL Protein conformational ensembles generation
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date created: 2026-03-25T10:20:45Z
date modified: 2026-03-25T10:31:13Z
description: # Protein Conformational ensembles generation ## Workflow included in the [ELIXIR 3D-Bioinfo](https://elixir-europe.org/communities/3d-bioinfo) Implementation Study: ### Building on PDBe-KB to chart and characterize the conformation landscape of native proteins This tutorial aims to illustrate the process of generating **protein conformational ensembles** from** 3D structures **and analysing its **molecular flexibility**, step by step, using the **BioExcel Building Blocks library (biobb)**. ## Conformational landscape of native proteins **Proteins** are **dynamic** systems that adopt multiple **conformational states**, a property essential for many **biological processes** (e.g. binding other proteins, nucleic acids, small molecule ligands, or switching between functionaly active and inactive states). Characterizing the different **conformational states** of proteins and the transitions between them is therefore critical for gaining insight into their **biological function** and can help explain the effects of genetic variants in **health** and **disease** and the action of drugs. **Structural biology** has become increasingly efficient in sampling the different **conformational states** of proteins. The **PDB** has currently archived more than **170,000 individual structures**, but over two thirds of these structures represent **multiple conformations** of the same or related protein, observed in different crystal forms, when interacting with other proteins or other macromolecules, or upon binding small molecule ligands. Charting this conformational diversity across the PDB can therefore be employed to build a useful approximation of the **conformational landscape** of native proteins. A number of resources and **tools** describing and characterizing various often complementary aspects of protein **conformational diversity** in known structures have been developed, notably by groups in Europe. These tools include algorithms with varying degree of sophistication, fo…
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description: IDR is based on OMERO and thus all what we show in this notebook can be easily adjusted for use against another OMERO server, e.g. your institutional OMERO server instance. The main objective of this notebook is to demonstrate how public resources such as the IDR can be used to train your neural network or validate software tools. The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August 2019: https://doi.org/10.1371/journal.pbio.3000388, considered several image segmenation packages, but they did not use the approach described in this notebook. We will analyse the data using Cellpose and compare the output with the original segmentation produced by the authors. StarDist was not considered by the authors. Our workflow shows how public repository can be accessed and data inside it used to validate software tools or new algorithms. We will use an image (id=6001247) referenced in the paper. The image can be viewed online in the Image Data Resource (IDR). We will use a predefined model from Cellpose as a starting point. Steps to access data from IDR could be re-used if you wish to create a new model (outside the scope of this notebook). ## Launch This notebook uses the [environment_cellpose.yml](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_4/environment_cellpose.yml) file. See [Setup](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_4/setup.md).
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description: # Protein Conformational ensembles generation ## Workflow included in the [ELIXIR 3D-Bioinfo](https://elixir-europe.org/communities/3d-bioinfo) Implementation Study: ### Building on PDBe-KB to chart and characterize the conformation landscape of native proteins This tutorial aims to illustrate the process of generating **protein conformational ensembles** from** 3D structures **and analysing its **molecular flexibility**, step by step, using the **BioExcel Building Blocks library (biobb)**. ## Conformational landscape of native proteins **Proteins** are **dynamic** systems that adopt multiple **conformational states**, a property essential for many **biological processes** (e.g. binding other proteins, nucleic acids, small molecule ligands, or switching between functionaly active and inactive states). Characterizing the different **conformational states** of proteins and the transitions between them is therefore critical for gaining insight into their **biological function** and can help explain the effects of genetic variants in **health** and **disease** and the action of drugs. **Structural biology** has become increasingly efficient in sampling the different **conformational states** of proteins. The **PDB** has currently archived more than **170,000 individual structures**, but over two thirds of these structures represent **multiple conformations** of the same or related protein, observed in different crystal forms, when interacting with other proteins or other macromolecules, or upon binding small molecule ligands. Charting this conformational diversity across the PDB can therefore be employed to build a useful approximation of the **conformational landscape** of native proteins. A number of resources and **tools** describing and characterizing various often complementary aspects of protein **conformational diversity** in known structures have been developed, notably by groups in Europe. These tools include algorithms with varying degree of sophistication, fo…
license: https://workflowhub.eu/workflows/490
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output: mycpptraj_flexserv_bd_traj_fitted.trr
output: mycpptraj_flexserv_dmd_rmsd.dat
output: mycpptraj_flexserv_dmd_traj_fitted.trr
output: mycpptraj_flexserv_nma_ensemble.trr
output: mycpptraj_flexserv_nma_rmsd.dat
output: mycpptraj_imods_ensemble.trr
output: mycpptraj_mask_backbone
output: mycpptraj_mask_ca.pdb
output: mycpptraj_meta_traj_fitted.crd
output: mycpptraj_meta_traj_rmsd.dat
output: mycpptraj_nolb_ensemble.trr
output: mycpptraj_nolb_rmsd.dat
output: mycpptraj_pcz_proj1.dcd
output: mycpptraj_prody_anm_traj.trr
output: mycpptraj_prody_rms.dat
output: mydmd_flexserv_dmd_ensemble.log
output: mydmd_flexserv_dmd_ensemble.mdcrd
output: myextract_model.pdb
output: myextract_monomer.pdb
output: mygmx_cluster.log
output: mygmx_cluster.xpm
output: mygmx_cluster.xvg
output: mygmx_concat.cluster.pdb
output: myimod_imc.pdb
output: myimod_imode_evecs.dat
output: mymake_gmx_ndx.ndx
output: mynma_flexserv_nma_ensemble.log
output: mynma_flexserv_nma_ensemble.mdcrd
output: mynolb_ensemble.pdb
output: mypcz_bfactor_all.dat
output: mypcz_bfactor_all.pdb
output: mypcz_collectivity.json
output: mypcz_evecs.json
output: mypcz_hinges_bfactor_report.json
output: mypcz_hinges_fcte_report.json
output: mypcz_proj1.crd
output: mypcz_report.json
output: mypcz_stiffness.json
output: mypdb.pdb
output: myprody_anm_traj.pdb
output: mytrjcat_concat_traj.trr
producer: https://workflowhub.eu/projects/11
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Validate a tool against IDR data: Load Image with labels from IDR, re-analyze using StarDist
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0003-0302-8247
creator: https://workflowhub.eu/people/144
date created: 2023-06-01T10:05:54Z
date modified: 2023-06-01T10:07:09Z
description: The notebook shows how to load an IDR image with labels. The image is referenced in the paper "NesSys: a novel method for accurate nuclear segmentation in 3D" published August 2019 in PLOS Biology: https://doi.org/10.1371/journal.pbio.3000388 and can be viewed online in the Image Data Resource. In this notebook, the image is loaded together with the labels and analyzed using StarDist. The StarDist analysis produces a segmentation, which is then viewed side-by-side with the original segmentations produced by the authors of the paper obtained via the loaded labels. ## Launch This notebook uses the [environment_stardist.yml](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_4/environment_stardist.yml) file. See [Setup](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_4/setup.md).
is part of: https://workflowhub.eu/collections/4
license: BSD-2-Clause
producer: https://workflowhub.eu/projects/69
sd publisher: about.workflowhub.eu
version: 1

Jupyter
type: ComputerLanguage

Use Public Resources to answer a biological question
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0003-0302-8247
creator: https://workflowhub.eu/people/144
date created: 2023-06-01T10:14:31Z
date modified: 2023-06-01T10:53:01Z
description: ## Learning Objectives - How to access genomic resource via its Python API - How to access image resource via its Python API - Relate image data to genomic data ## Diabetes related genes expressed in pancreas This notebook looks at the question **Which diabetes related genes are expressed in the pancreas?** Tissue and disease can be modified. Steps: - Query [humanmine.org](https://www.humanmine.org/humanmine), an integrated database of Homo sapiens genomic data using the intermine API to find the genes. - Using the list of found genes, search in the [Image Data Resource (IDR)](https://idr.openmicroscopy.org/) for images linked to the genes, tissue and disease. - Analyse the images found. ## Launch This notebook uses the [environment.yml](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_4/environment.yml) file. See [Setup](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_4/setup.md).
is part of: https://workflowhub.eu/collections/4
keywords: imaging, Python
license: BSD-2-Clause
producer: https://workflowhub.eu/projects/69
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 1

Load ome.zarr Image with labels from public S3 repositories, analyze in parallel using Cellpose and compare results
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0003-0302-8247
creator: https://workflowhub.eu/people/144
date created: 2023-06-01T10:30:51Z
date modified: 2023-07-04T00:40:25Z
description: ## Learning objectives - Read data to analyse from an object store. - Analyse data in parallel using Dask. - Show how to use public resources to train neural network. - Load labels associated to the original data - Compare results with ground truth. The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August 2019: https://doi.org/10.1371/journal.pbio.3000388, considered several image segmenation packages, but they did not use the approach described in this notebook. We will analyse the data using [Cellpose](https://www.cellpose.org/) and compare the output with the original segmentation produced by the authors. Cellpose was not considered by the authors. Our workflow shows how public repository can be accessed and data inside it used to validate software tools or new algorithms. We will use a predefined model from Cellpose as a starting point. ## Launch This notebook uses the [environment.yml](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_5/environment.yml) file. See [Setup](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_5/setup.md).
keywords: OME-Zarr, Python, imaging, image processing, Machine Learning, S3
license: BSD-2-Clause
producer: https://workflowhub.eu/projects/69
sd publisher: about.workflowhub.eu
version: 1

Load ome.zarr Image with labels from a public S3 repository, analyze using StarDist and compare results
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0003-0302-8247
creator: https://workflowhub.eu/people/144
date created: 2023-06-01T10:49:40Z
date modified: 2023-06-01T10:50:42Z
description: The image is referenced in the paper "NesSys: a novel method for accurate nuclear segmentation in 3D" published August 2019 in PLOS Biology: https://doi.org/10.1371/journal.pbio.3000388 and can be viewed online in the [Image Data Resource](https://idr.openmicroscopy.org/webclient/?show=image-6001247). This original image was converted into the Zarr format. The analysis results produced by the authors of the paper were converted into labels and linked to the Zarr file which was placed into a public S3 repository. In this notebook, the Zarr file is then loaded together with the labels from the S3 storage and analyzed using [StarDist](https://github.com/stardist/stardist). The StarDist analysis produces a segmentation, which is then viewed side-by-side with the original segmentations produced by the authors of the paper obtained via the loaded labels. ## Launch This notebook uses the [environment_stardist.yml](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_5/environment_stardist.yml) file. See [Setup](https://github.com/ome/EMBL-EBI-imaging-course-05-2023/blob/main/Day_5/setup.md).
keywords: OME-Zarr, Python, Machine Learning, imaging, S3
license: BSD-2-Clause
producer: https://workflowhub.eu/projects/69
sd publisher: about.workflowhub.eu
version: 1

Population and community metrics calculation from Biodiversity data
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://workflowhub.eu/people/51
creator: https://workflowhub.eu/people/52
date created: 2020-07-24T13:00:50Z
date modified: 2024-12-09T08:46:05Z
description: Galaxy-E (ecology.usegalaxy.eu) workflow to calculate species presence / absence, community metrics and compute generalized linear models to identify effects and significativity of these effects on biodiversity.
input: 0_Input Dataset
input: 1_Input Dataset
is based on: https://workflowhub.eu/workflows/49
is part of: https://workflowhub.eu/collections/33
keywords: Community_metrics, Presence_absence, GLM, Ecology, Biodiversity, Species abundance, Modeling, Statistics
license: MIT
producer: https://workflowhub.eu/projects/19
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 2

Genomics - Read pre-processing without downloading from SRA
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Andrew Lonie
creator: Anton Nekrutenko
creator: Bert Droesbeke
creator: Björn Grüning
creator: Dannon Baker
creator: Dave Bouvier
creator: Delphine Larivière
creator: Frederik Coppens
creator: Gildas Le Corguillé
creator: Ignacio Eguinoa
creator: James Taylor
creator: John Chilton
creator: Marius van den Beek
creator: Nate Coraor
creator: Nicholas Keener
creator: Sergei Kosakovsky Pond
creator: Simon Gladman
creator: Steven Weaver
creator: Wolfgang Maier
date created: 2020-04-10T12:06:00Z
date modified: 2023-01-16T13:39:49Z
description: Preprocessing of raw SARS-CoV-2 reads. This workflow contains an alternate starting point to avoid the data to be downloaded from the NCBI SRA. More info can be found at https://covid19.galaxyproject.org/genomics/
input: 0_Input Dataset Collection
input: 1_Input Dataset Collection
keywords: covid-19
license: MIT
producer: https://workflowhub.eu/projects/3
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Andrew Lonie
type: Person

Anton Nekrutenko
type: Person

Bert Droesbeke
type: Person

Björn Grüning
type: Person

Dannon Baker
type: Person

Dave Bouvier
type: Person

Delphine Larivière
type: Person

Frederik Coppens
type: Person

Gildas Le Corguillé
type: Person

Ignacio Eguinoa
type: Person

James Taylor
type: Person

John Chilton
type: Person

Marius van den Beek
type: Person

Nate Coraor
type: Person

Nicholas Keener
type: Person

Sergei Kosakovsky Pond
type: Person

Simon Gladman
type: Person

Steven Weaver
type: Person

Wolfgang Maier
type: Person

Galaxy
type: ComputerLanguage

0_Input Dataset
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conforms to: 1.0-RELEASE

1_Input Dataset
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conforms to: 1.0-RELEASE

2_Input Dataset
type: FormalParameter
conforms to: 1.0-RELEASE

Milad Miladi
type: Person

Galaxy
type: ComputerLanguage

draft.fa
type: FormalParameter
conforms to: 1.0-RELEASE

fast5_files.tar.gz
type: FormalParameter
conforms to: 1.0-RELEASE

reads.fasta
type: FormalParameter
conforms to: 1.0-RELEASE

R markdown
type: ComputerLanguage
alternate name: RMD

Performance evaluation of Katdetectr and other kataegis detection packages
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0001-7767-7923
creator: https://orcid.org/0000-0002-7513-6813
date created: 2023-06-07T17:32:16Z
date modified: 2023-06-07T17:52:17Z
description: # Introduction This repository contains all the custom scripts used in the evaluation and comparison of [Katdetectr](https://github.com/ErasmusMC-CCBC/evaluation_katdetectr/tree/main) as described in the corresponding Technical Note (under submission). # Usage All required files were deposited on [Zenodo](https://zenodo.org/record/6623289#.YqBxHi8Rr0o%5D). These can directly be downloaded using `zen4R` and be used as input. ```R # Increase the timeout (due to some large files). options(timeout=5000) # Download the required files into the data/ folder (~1GB). zen4R::download_zenodo(doi = "10.5281/zenodo.6810477", path = 'data/') ```
license: LGPL-3.0
producer: https://workflowhub.eu/projects/162
programming language: R markdown
sd publisher: about.workflowhub.eu
version: 1

Quarto Markdown
type: ComputerLanguage
alternate name: qmd

BY-COVID WP5 T5.2 Baseline Use Case
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Nina Van Goethem
creator: Santiago Royo-Sierra
creator: Enrique Bernal-Delgado
creator: Marjan Meurisse
creator: Francisco Estupiñan-Romero
creator: Javier González Galindo
creator: Natalia Martínez-Lizaga
date created: 2024-07-26T13:08:32Z
date modified: 2024-07-26T16:03:14Z
description: This publication corresponds to the Research Objects (RO) of the Baseline Use Case proposed in T.5.2 (WP5) in the BY-COVID project on “COVID-19 Vaccine(s) effectiveness in preventing SARS-CoV-2 infection”.
is based on: https://workflowhub.eu/workflows/502
keywords: covid-19, vaccines, comparative effectiveness, causal inference, international comparison, SARS-CoV-2, common data model, directed acyclic graph, synthetic data
license: CC-BY-4.0
producer: BY-COVID Baseline Use Case: SARS-CoV-2 Vaccine(s) effectiveness in preventing SARS-CoV-2 infection
programming language: Quarto Markdown
sd publisher: about.workflowhub.eu
version: 4

Snakemake
type: ComputerLanguage

Purge retained haplotypes using Purge-Dups
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Tom Brown
date created: 2024-03-16T07:45:11Z
date modified: 2024-03-20T16:10:05Z
description: ## Purge dups This snakemake pipeline is designed to be run using as input a contig-level genome and pacbio reads. This pipeline has been tested with `snakemake v7.32.4`. Raw long-read sequencing files and the input contig genome assembly must be given in the `config.yaml` file. To execute the workflow run: `snakemake --use-conda --cores N` Or configure the cluster.json and run using the `./run_cluster` command
is based on: https://workflowhub.eu/workflows/506
is part of: ERGA Assembly Snakemake HiFi & HiC Pipelines
is part of: https://workflowhub.eu/collections/33
keywords: Assembly, Genomics, Snakemake, Bioinformatics, Genome assembly, Biodiversity
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/163
producer: https://workflowhub.eu/projects/202
programming language: Snakemake
sd publisher: about.workflowhub.eu
version: 2

ONT --Tutorial-Nanopolish-variants
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Milad Miladi
date created: 2020-08-05T12:52:50Z
date modified: 2023-02-13T14:06:46Z
description: Basic workflows inspired by the Nanopolish tutorials
input: draft.fa
input: fast5_files.tar.gz
input: reads.fasta
license: MIT
producer: https://workflowhub.eu/projects/17
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Milad Miladi
type: Person

Galaxy
type: ComputerLanguage

PkorrP19E3_ONT_fast5.tar.gz
type: FormalParameter
conforms to: 1.0-RELEASE

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

collision_info
type: FormalParameter
conforms to: 1.0-RELEASE

db_name
type: FormalParameter
conforms to: 1.0-RELEASE

db_path
type: FormalParameter
conforms to: 1.0-RELEASE

gnps_file
type: FormalParameter
conforms to: 1.0-RELEASE

hmdb_file
type: FormalParameter
conforms to: 1.0-RELEASE

mbank_file
type: FormalParameter
conforms to: 1.0-RELEASE

mzml_files
type: FormalParameter
conforms to: 1.0-RELEASE

ppmx
type: FormalParameter
conforms to: 1.0-RELEASE

python_script
type: FormalParameter
conforms to: 1.0-RELEASE

r_script
type: FormalParameter
conforms to: 1.0-RELEASE

candidate_files
type: FormalParameter
conforms to: 1.0-RELEASE

result
type: FormalParameter
conforms to: 1.0-RELEASE

Metabolome Annotation Workflow (MAW)
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Christoph Steinbeck
creator: Maria Sorokina
creator: Kristian Peters
creator: Mahnoor Zulfiqar
creator: Luiz Gadelha
creator: https://workflowhub.eu/people/70
date created: 2023-08-01T14:21:50Z
date modified: 2023-08-01T14:22:09Z
description: This repository hosts Metabolome Annotation Workflow (MAW). The workflow takes MS2 .mzML format data files as an input in R. It performs spectral database dereplication using R Package Spectra and compound database dereplication using SIRIUS OR MetFrag . Final candidate selection is done in Python using RDKit and PubChemPy.
input: collision_info
input: db_name
input: db_path
input: gnps_file
input: hmdb_file
input: mbank_file
input: mzml_files
input: ppmx
input: python_script
input: r_script
is based on: https://workflowhub.eu/workflows/510
keywords: Metabolomics, Annotation, mass-spectrometry, identification, Bioinformatics, FAIR workflows, workflow, gnps, massbank, hmdb, spectra, rdkit, Cheminformatics
license: MIT
output: candidate_files
output: result
producer: Metabolomics-Reproducibility
programming language: Common Workflow Language
sd publisher: about.workflowhub.eu
version: 2

Ana Ayala
type: Person

Dennis Trolle
type: Person

Don Pierson
type: Person

Inmaculada Jiménez-Navarro
type: Person

Javier Senent-Aparicio
type: Person

Timing of spring events changes under modelled future climate scenarios in a mesotrophic lake
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Jorrit Mesman
creator: Ana Ayala
creator: Dennis Trolle
creator: Don Pierson
creator: Inmaculada Jiménez-Navarro
creator: Javier Senent-Aparicio
date created: 2024-01-16T13:18:14Z
date modified: 2024-01-16T13:19:15Z
description: Simulations and figures supporting the manuscript "Timing of spring events changes under modelled future climate scenarios in a mesotrophic lake"
is based on: https://workflowhub.eu/workflows/511
license: CC-BY-4.0
producer: Lake Erken modelling setup
sd publisher: about.workflowhub.eu
version: 5

Mike Thang
type: Person

Sarah Williams
type: Person

Valentine Murigneaux
type: Person

Galaxy
type: ComputerLanguage

Sample
type: FormalParameter
conforms to: 1.0-RELEASE

barcodes.tsv
type: FormalParameter
conforms to: 1.0-RELEASE

genes.tsv
type: FormalParameter
conforms to: 1.0-RELEASE

matrix.mtx
type: FormalParameter
conforms to: 1.0-RELEASE

AnnData Loaded
type: FormalParameter
conforms to: 1.0-RELEASE

scRNAseq: Load counts matrix
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Mike Thang
creator: Sarah Williams
creator: Valentine Murigneaux
date created: 2024-05-30T05:44:52Z
date modified: 2024-05-30T05:44:52Z
description: Loads a single cell counts matrix into an annData format - adding a column called sample with the sample name. (Input format - matrix.mtx, features.tsv and barcodes.tsv)
input: Sample
input: barcodes.tsv
input: genes.tsv
input: matrix.mtx
is based on: https://workflowhub.eu/workflows/512
is part of: https://workflowhub.eu/collections/9
keywords: scRNAseq
license: MIT
output: AnnData Loaded
producer: https://workflowhub.eu/projects/41
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 2

Mike Thang
type: Person

Sarah Williams
type: Person

Valentine Murigneaux
type: Person

Galaxy
type: ComputerLanguage

#main/Barcode Whitelist file
type: FormalParameter
conforms to: 1.0-RELEASE

#main/Expected Cells
type: FormalParameter
conforms to: 1.0-RELEASE

#main/Paired Fastqs for one sample
type: FormalParameter
conforms to: 1.0-RELEASE

#main/Reference Genome fasta
type: FormalParameter
conforms to: 1.0-RELEASE

#main/Reference annotation
type: FormalParameter
conforms to: 1.0-RELEASE

#main/Sample
type: FormalParameter
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#main/AnnData Loaded
type: FormalParameter
conforms to: 1.0-RELEASE

#main/RNA STARSolo on input dataset(s): Alignments
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type: FormalParameter
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type: FormalParameter
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#main/RNA STARSolo on input dataset(s): Genes filtered
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#main/RNA STARSolo on input dataset(s): Matrix Gene Counts filtered
type: FormalParameter
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#main/RNA STARSolo on input dataset(s): log
type: FormalParameter
conforms to: 1.0-RELEASE

scRNAseq: Count and Load with starSOLO
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Mike Thang
creator: Sarah Williams
creator: Valentine Murigneaux
date created: 2024-05-30T05:47:35Z
date modified: 2024-05-30T05:47:35Z
description: Takes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.
input: #main/Barcode Whitelist file
input: #main/Expected Cells
input: #main/Paired Fastqs for one sample
input: #main/Reference Genome fasta
input: #main/Reference annotation
input: #main/Sample
is based on: https://workflowhub.eu/workflows/513
is part of: https://workflowhub.eu/collections/9
keywords: scRNAseq
license: MIT
output: #main/AnnData Loaded
output: #main/RNA STARSolo on input dataset(s): Alignments
output: #main/RNA STARSolo on input dataset(s): Barcode/Feature Statistic Summaries
output: #main/RNA STARSolo on input dataset(s): Barcodes filtered
output: #main/RNA STARSolo on input dataset(s): Genes filtered
output: #main/RNA STARSolo on input dataset(s): Matrix Gene Counts filtered
output: #main/RNA STARSolo on input dataset(s): log
producer: https://workflowhub.eu/projects/41
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 3

Mike Thang
type: Person

Sarah Williams
type: Person

Valentine Murigneaux
type: Person

Galaxy
type: ComputerLanguage

scRNAseq Single Sample Processing Counts Matrix
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Mike Thang
creator: Sarah Williams
creator: Valentine Murigneaux
date created: 2024-05-30T05:19:29Z
date modified: 2024-05-30T05:55:19Z
description: Take a scRNAseq counts matrix from a single sample, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData object. Depreciated: use individual workflows insead for multiple samples
is based on: https://workflowhub.eu/workflows/514
is part of: https://workflowhub.eu/collections/9
keywords: scRNAseq
license: MIT
producer: https://workflowhub.eu/projects/41
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 3

COMPSs
type: ComputerLanguage

Java COMPSs LU Factorization for Sparse Matrices
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing)
creator: https://workflowhub.eu/people/556
date created: 2023-06-23T14:03:17Z
date modified: 2023-11-24T08:43:23Z
description: **Name:** SparseLU **Contact Person:** support-compss@bsc.es **Access Level:** public **License Agreement:** Apache2 **Platform:** COMPSs # Description The Sparse LU application computes an LU matrix factorization on a sparse blocked matrix. The matrix size (number of blocks) and the block size are parameters of the application. As the algorithm progresses, the area of the matrix that is accessed is smaller; concretely, at each iteration, the 0th row and column of the current matrix are discarded. On the other hand, due to the sparseness of the matrix, some of its blocks might not be allocated and, therefore, no work is generated for them. When executed with COMPSs, Sparse LU produces several types of task with different granularity and numerous dependencies between them. # Versions There are three versions of Sparse LU, depending on the data types used to store the blocks. ## Version 1 ''files'', where the matrix blocks are stored in files. ## Version 2 ''objects'', where the matrix blocks are represented by objects. ## Version 3 ''arrays'', where the matrix blocks are stored in arrays. # Execution instructions Usage: ``` runcompss sparseLU.files.SparseLU numberOfBlocks blockSize runcompss sparseLU.objects.SparseLU numberOfBlocks blockSize runcompss sparseLU.arrays.SparseLU numberOfBlocks blockSize ``` where: * numberOfBlocks: Number of blocks inside each matrix * blockSize: Size of each block # Execution Example ``` runcompss sparseLU.objects.SparseLU 16 4 runcompss sparseLU.files.SparseLU 16 4 runcompss sparseLU.arrays.SparseLU 16 4 ``` # Build ## Option 1: Native java ``` cd application_sources/; javac src/main/java/sparseLU/*/*.java cd src/main/java/; jar cf sparseLU.jar sparseLU/ cd ../../../; mv src/main/java/sparseLU.jar jar/ ``` ## Option 2: Maven ``` cd application_sources/ mvn clean package ```
keywords: Java, COMPSs, Tutorial, Example, Marenostrum IV, Supercomputer, data_persistence
license: Apache-2.0
producer: https://workflowhub.eu/projects/102
producer: https://workflowhub.eu/projects/172
programming language: COMPSs
sd publisher: about.workflowhub.eu
version: 1

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Life Science cross-RI (Research Infrastructure) project
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Elisabetta Spinazzola
date created: 2023-06-27T07:39:45Z
date modified: 2023-06-27T09:25:04Z
description: The project allowed us to manage and build structured code scripts on the Jupyter Notebook, a simple web application which is user-friendly, flexible to use in the research community. The script is developed to address the specific needs of research between different platforms of dataset. These stakeholders have developed their own platforms for the annotation and standardisation of both data and metadata produced within their respective field. -The INFRAFRONTIER - European Mutant Mouse Archive (EMMA) comprises over 7200 mutant mouse lines that are extensively integrated and enriched with other public dataset. -The EU-OpenScreen offers compound screening protocols containing several metadata and will contribute to the development of tools for linking to the chemical entity database. -The IDR Image Data Resource is a public repository of reference image datasets from published scientific studies, where the community can submit, search and access high-quality bio-image data. -The CIM-XNAT is an XNAT deployment of the Molecular Imaging Center at UniTo that offers a suite of tools for uploading preclinical images. To address the challenges of integrating several EU-RI datasets with focus on preclinical and discovery research bioimaging, our aim is to develop cross researching queries through a web based interface to combine the resources of the RIs for integrating the information associated with data belonging to the involved RIs. Furthermore, the open-source tool provides users with free, open access to collections of datasets distributed over multiple sources that result from searches by specific keywords. The script allows the cross research in different fields of research as: Species, Strain, Gene, Cell line, Disease model, Chemical Compound. The novel aspects of this tool are mainly: a) user friendly, e.g. the user has the flexibility to research among the dataset easily with a simple API, intuitive for researchers and biomedical users. b) the possibility of making…
license: CC-BY-4.0
producer: EOSC-Life WP3 OC Team, cross RI project
programming language: Jupyter
sd publisher: about.workflowhub.eu
version: 1

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VVV2_align_SE
type: ComputationalWorkflow
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conforms to: 1.0-RELEASE
creator: https://orcid.org/0000-0002-1103-2425
date created: 2023-06-27T14:41:53Z
date modified: 2025-06-19T10:24:37Z
description: SINGLE-END workflow. Align reads on fasta reference/assembly using bwa mem, get a consensus, variants, mutation explanations. IMPORTANT: * For "bcftools call" consensus step, the --ploidy file is in "Données partagées" (Shared Data) and must be imported in your history to use the worflow by providing this file (tells bcftools to consider haploid variant calling). * SELECT the mot ADAPTED VADR MODEL for annotation (see vadr parameters).
input: Ploidy file (Y for happloid)
keywords: single-end, Annotation, variant, Virus, variant_calling, high-throughput_sequencing_analysis, Galaxy, Bioinformatics, SNPs, variant calling
license: GPL-3.0
output: _anonymous_output_1
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producer: https://workflowhub.eu/projects/170
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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VVV2_align_PE
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creator: https://orcid.org/0000-0002-1103-2425
date created: 2023-06-28T09:52:35Z
date modified: 2025-06-19T10:23:49Z
description: PAIRED-END workflow. Align reads on fasta reference/assembly using bwa mem, get a consensus, variants, mutation explanations. IMPORTANT: * For "bcftools call" consensus step, the --ploidy file is in "Données partagées" (Shared Data) and must be imported in your history to use the worflow by providing this file (tells bcftools to consider haploid variant calling). * SELECT THE MOST ADAPTED VADR MODEL for annotation (see vadr parameters).
input: Ploidy file (Y for happloid)
keywords: paired-end, variant_calling, Annotation, Virus, Alignment, Bioinformatics, Galaxy, SNPs, covid-19, variant calling, workflow
license: GPL-3.0
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producer: https://workflowhub.eu/projects/170
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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SARS-CoV-2 Illumina Amplicon pipeline - SANBI - v1.2
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creator: Bradley W. Langhorst
creator: Peter van Heusden
date created: 2023-06-28T11:42:46Z
date modified: 2023-06-30T05:14:33Z
description: SARS-CoV-2 variant prediction using Read It And Keep, fastp, bbmap and iVar
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input: Paired read collection for samples
input: Primer BED
input: Read fraction to call variant
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keywords: covid-19, ARTIC, SARS-CoV-2, SANBI
license: MIT
output: _anonymous_output_1
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output: combined_coverage
output: combined_multifasta
output: ivar_consensus_genome
output: ivar_variants_tabular
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producer: https://workflowhub.eu/projects/171
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

ONT -- Assembly-Flye-AhrensLab
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date created: 2020-08-05T13:01:18Z
date modified: 2023-02-13T14:06:46Z
description: Genome assembly: Flye-based WF for highly repetitive genomes [Schmid et al. NAR 2018]
input: ont assembly flye ahrenslab-inputs-ftp://biftp.informatik.uni-freiburg.de/pub/T0/Ahrens/SRR6982805.fastq
input: PkorrP19E3_ONT_fast5.tar.gz
keywords: name:ONT, ONT
license: MIT
producer: https://workflowhub.eu/projects/17
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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Galaxy
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input: Aligned reads (BAM)
input: Amplicons
input: Consensus Genomes
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keywords: SARS-CoV-2, SANBI
license: CC-BY-4.0
producer: https://workflowhub.eu/projects/171
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

Galaxy
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input: Maximum read length
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input: Reference genome
input: Sequence Reads
keywords: SARS-CoV-2, SANBI, nanopore
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producer: https://workflowhub.eu/projects/171
programming language: Galaxy
sd publisher: about.workflowhub.eu
version: 1

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CWL-based RNA-Seq workflow
type: ComputationalWorkflow
type: SoftwareSourceCode
conforms to: 1.0-RELEASE
creator: Konstantinos Kyritsis
creator: https://orcid.org/0000-0002-0222-4273
creator: 0000-0003-2502-612X
date created: 2023-07-05T08:44:44Z
date modified: 2023-07-05T09:16:36Z
description: A CWL-based pipeline for processing RNA-Seq data (FASTQ format) and performing differential gene/transcript expression analysis. On the respective GitHub folder are available: - The CWL wrappers for the workflow - A pre-configured YAML template, based on validation analysis of publicly available HTS data - A table of metadata (``mrna_cll_subsets_phenotypes.csv``), based on the same validation analysis, to serve as an input example for the design of comparisons during differential expression analysis Briefly, the workflow performs the following steps: 1. Quality control of Illumina reads (FastQC) 2. Trimming of the reads (e.g., removal of adapter and/or low quality sequences) (Trim galore) 3. (Optional) custom processing of the reads using FASTA/Q Trimmer (part of the FASTX-toolkit) 4. Mapping to reference genome (HISAT2) 5. Convertion of mapped reads from SAM (Sequence Alignment Map) to BAM (Binary Alignment Map) format (samtools) 6. Sorting mapped reads based on chromosomal coordinates (samtools) Subsequently, two independent workflows are implemented for differential expression analysis at the transcript and gene level. **First**, following the [reference protocol](https://doi.org/10.1038/nprot.2016.095) for HISAT, StringTie and Ballgown transcript expression analysis, StringTie along with a reference transcript annotation GTF (Gene Transfer Format) file (if one is available) is used to: - Assemble transcripts for each RNA-Seq sample using the previous read alignments (BAM files) - Generate a global, non-redundant set of transcripts observed in any of the RNA-Seq samples - Estimate transcript abundances and generate read coverage tables for each RNA-Seq sample, based on the global, merged set of transcripts (rather than the reference) which is observed across all samples Ballgown program is then used to load the coverage tables generated in the previous step and perform statistical analyses for differential expression at the transcript level. Notably, the StringTi…
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input: samtools_view_threads
input: samtools_view_uncompressed

Common Workflow Language
type: ComputerLanguage
alternate name: CWL

ChIPQC_annotation
type: FormalParameter
conforms to: 1.0-RELEASE

ChIPQC_bCount
type: FormalParameter
conforms to: 1.0-RELEASE

ChIPQC_blacklist
type: FormalParameter
conforms to: 1.0-RELEASE

ChIPQC_consensus
type: FormalParameter
conforms to: 1.0-RELEASE

ChIPQC_facetBy
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_bParallel
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_background
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_blacklist
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_consensus
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_cores
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_design
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_filterFun
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_greylist
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_library
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_low_read_count_filter
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_minOverlap
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_normalization
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_reorderMeta_factor
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_reorderMeta_value
type: FormalParameter
conforms to: 1.0-RELEASE

DiffBind_retrieve_consensus
type: FormalParameter
conforms to: 1.0-RELEASE

bamCoverage_effective_genome_size
type: FormalParameter
conforms to: 1.0-RELEASE

bamCoverage_extendReads
type: FormalParameter
conforms to: 1.0-RELEASE

bamCoverage_normalizeUsing
type: FormalParameter
conforms to: 1.0-RELEASE

bamCoverage_threads
type: FormalParameter
conforms to: 1.0-RELEASE

blackListFile
type: FormalParameter
conforms to: 1.0-RELEASE

computeMatrix_downstream
type: FormalParameter
conforms to: 1.0-RELEASE

computeMatrix_outFileSortedRegions
type: FormalParameter
conforms to: 1.0-RELEASE

computeMatrix_outputFile
type: FormalParameter
conforms to: 1.0-RELEASE

computeMatrix_regions
type: FormalParameter
conforms to: 1.0-RELEASE

computeMatrix_threads
type: FormalParameter
conforms to: 1.0-RELEASE

computeMatrix_upstream
type: FormalParameter
conforms to: 1.0-RELEASE

hisat2_idx_basename
type: FormalParameter
conforms to: 1.0-RELEASE

hisat2_idx_directory
type: FormalParameter
conforms to: 1.0-RELEASE

hisat2_num_of_threads
type: FormalParameter
conforms to: 1.0-RELEASE