{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BYY-Eb9dKUIi", "outputId": "41a3fc80-917f-4fa9-e98b-d0d56f9aa7f4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting jbrowse-jupyter\n", " Downloading jbrowse_jupyter-1.2.1-py3-none-any.whl (22 kB)\n", "Collecting pytest>=6.2.5\n", " Downloading pytest-7.0.1-py3-none-any.whl (296 kB)\n", "\u001b[K |████████████████████████████████| 296 kB 10.8 MB/s \n", "\u001b[?25hCollecting dash==2.0.0\n", " Downloading dash-2.0.0-py3-none-any.whl (7.3 MB)\n", "\u001b[K |████████████████████████████████| 7.3 MB 29.4 MB/s \n", "\u001b[?25hCollecting flake8>=4.0.1\n", " Downloading flake8-4.0.1-py2.py3-none-any.whl (64 kB)\n", "\u001b[K |████████████████████████████████| 64 kB 1.6 MB/s \n", "\u001b[?25hCollecting 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satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->jupyter-dash>=0.4.0->jbrowse-jupyter) (2.10)\n", "Building wheels for collected packages: dash-core-components, dash-html-components, dash-table, retrying\n", " Building wheel for dash-core-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for dash-core-components: filename=dash_core_components-2.0.0-py3-none-any.whl size=3822 sha256=8265edfa47273dc954f7f3c20f0d52ba56c0592fe07bcc6f74f9e006a8a8d155\n", " Stored in directory: /root/.cache/pip/wheels/00/f9/c7/1a6437d794ed753ea9bc9079e761d4fc803a1f1f5d3697b9ec\n", " Building wheel for dash-html-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for dash-html-components: filename=dash_html_components-2.0.0-py3-none-any.whl size=4090 sha256=0d1db5cfa4ba42752f156d6f1a562fa75e3fb6a50e8bb90c40dce635cba6790b\n", " Stored in directory: /root/.cache/pip/wheels/ec/6b/81/05aceabd8b27f724e2c96784016287cc1bfbc349ebfda451de\n", " Building wheel for dash-table (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for dash-table: filename=dash_table-5.0.0-py3-none-any.whl size=3912 sha256=c6c041525c7205a215a7b4f551fbd91f80e519c205d3a56bbfaac90315e97c82\n", " Stored in directory: /root/.cache/pip/wheels/85/5d/4e/7c276b57992951dbe770bf5caad6448d0539c510663aefd2e2\n", " Building wheel for retrying (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for retrying: filename=retrying-1.3.3-py3-none-any.whl size=11447 sha256=72c0f939f5572af84c8f05ce4d9c5bbb5e18b0cedfd314deb74ac303eec458b7\n", " Stored in directory: /root/.cache/pip/wheels/f9/8d/8d/f6af3f7f9eea3553bc2fe6d53e4b287dad18b06a861ac56ddf\n", "Successfully built dash-core-components dash-html-components dash-table retrying\n", "Installing collected packages: brotli, importlib-metadata, flask-compress, dash-table, dash-html-components, dash-core-components, retrying, pyflakes, pycodestyle, pluggy, mccabe, dash, ansi2html, pytest, jupyter-dash, flake8, dash-jbrowse, jbrowse-jupyter\n", " Attempting uninstall: importlib-metadata\n", " Found existing installation: importlib-metadata 4.11.0\n", " Uninstalling importlib-metadata-4.11.0:\n", " Successfully uninstalled importlib-metadata-4.11.0\n", " Attempting uninstall: pluggy\n", " Found existing installation: pluggy 0.7.1\n", " Uninstalling pluggy-0.7.1:\n", " Successfully uninstalled pluggy-0.7.1\n", " Attempting uninstall: pytest\n", " Found existing installation: pytest 3.6.4\n", " Uninstalling pytest-3.6.4:\n", " Successfully uninstalled pytest-3.6.4\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "markdown 3.3.6 requires importlib-metadata>=4.4; python_version < \"3.10\", but you have importlib-metadata 4.2.0 which is incompatible.\n", "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n", "Successfully installed ansi2html-1.7.0 brotli-1.0.9 dash-2.0.0 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-jbrowse-1.0.1 dash-table-5.0.0 flake8-4.0.1 flask-compress-1.10.1 importlib-metadata-4.2.0 jbrowse-jupyter-1.2.1 jupyter-dash-0.4.1 mccabe-0.6.1 pluggy-1.0.0 pycodestyle-2.8.0 pyflakes-2.4.0 pytest-7.0.1 retrying-1.3.3\n" ] } ], "source": [ "!pip install jbrowse-jupyter" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HetA2JAhKuf5" }, "outputs": [], "source": [ "from dash import html, Dash, dcc\n", "from dash.dependencies import Input, Output\n", "from jbrowse_jupyter import create, create_component" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QdWiYfiIKyHv" }, "outputs": [], "source": [ " app = Dash(__name__)\n", " server = app.server" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XES1Tx0KK144" }, "outputs": [], "source": [ "# locations of interest and name\n", "# sources: \n", "# https://jbrowse.org/demos/cancer-demo-2020/#embed\n", "# https://genome.cshlp.org/content/early/2018/06/28/gr.231100.117.abstract\n", "locations = [\n", " {\"value\": \"NC_000014.8:50234871..50253103\", \"label\": \"KLHDC2\"\n", " },\n", " {\"value\": \"NC_000017.10:76670129..76778424\", \"label\": \"CYTH1\"},\n", " {\"value\": \"NC_000020.10:34213953..34252822\", \"label\": \"CPNE1\"},\n", " {\"value\": \"NC_000017.10:38060848..38074903\", \"label\": \"GSDMB\"},\n", " {\"value\": \"8:116962736..117337297\", \"label\": \"LINC00536\"},\n", " {\"value\": \"8:121547996..121824286\", \"label\": \"SNTB1\"},\n", " {\"value\": \"8:117654369..117768062\", \"label\": \"EIF3H\"},\n", " {\"value\": \"NC_000020.10:47240790..47444536\", \"label\": \"PREX1\"},\n", " {\"value\": \"8:125500735..125551329\", \"label\": \"TATDN1\"},\n", " {\"value\": \"8:128,806,779..129,113,499\", \"label\": \"PVT1\"}\n", "]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eMuOH8KBMSxJ" }, "outputs": [], "source": [ "# track data, track name, track id\n", "tracks = [\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/skbr3/reads_lr_skbr3.fa_ngmlr-0.2.3_mapped.splitters.bam\", \"SKBR3 pacbio (NGMLR) splitters only\",\"ngmlr_splitters\"),\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/skbr3/reads_lr_skbr3.fa_ngmlr-0.2.3_mapped.splitters.cram\", \"SKBR3 pacbio (NGMLR) splitters only (CRAM)\",\"ngmlr_splitters_cram\"),\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/skbr3/reads_lr_skbr3.fa_ngmlr-0.2.3_mapped.down.bam\", \"SKBR3 pacbio (NGMLR) 0.3 downsample\",\"ngmlr_downsample\"),\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/skbr3/reads_lr_skbr3.fa_ngmlr-0.2.3_mapped.down.cram\", \"SKBR3 pacbio (NGMLR) 0.3 downsample (CRAM)\",\"reads_lr_skbr3.fa_ngmlr-0.2.3_mapped.down\"),\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/reads_lr_skbr3.fa_ngmlr-0.2.3_mapped.bam.regions.bw\", \"SKBR3 pacbio coverage (NGMLR)\",\"ngmlr_cov\"),\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/reads_lr_skbr3.fa_ngmlr-0.2.3_mapped.bam.sniffles1kb_auto_l8_s5_noalt.vcf.gz\", \"SKBR3 pacbio (sniffles VCF)\",\"breast_cancer_sniffles_hg19\"),\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/skbr3/SKBR3_550bp_pcrFREE_S1_L001_AND_L002_R1_001.101bp.bwamem.ill.mapped.sort.bam\", \"SKBR3 illumina bwamem (BAM)\",\"SKBR3 illumina bwamem (BAM)-id\"),\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/skbr3/SKBR3_550bp_pcrFREE_S1_L001_AND_L002_R1_001.101bp.bwamem.ill.mapped.sort.cram\", \"SKBR3 illumina bwamem (CRAM)\",\"SKBR3 illumina bwamem (CRAM)-id\"),\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/reads_lr_skbr3.fa_ngmlr-0.2.3_mapped.bam\", \"SKBR3 pacbio (NGMLR)\",\"ngmlr\"),\n", " (\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/GRCh37_latest_genomic.sort.gff.gz\", \"NCBI RefSeq (GFF3Tabix)\",\"ncbi_gff_hg19\")\n", "]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qxwK1FBHPpDr" }, "outputs": [], "source": [ "conf_lgv = create(\"LGV\", genome=\"hg19\")\n", "conf_cgv = create(\"CGV\", genome=\"hg19\")\n", "for track in tracks:\n", " data = track[0]\n", " track_name = track[1]\n", " track_id = track[2]\n", " conf_lgv.add_track(data, name=track_name, track_id=track_id)\n", "# adding a text search adapter for LGV \n", "ix = \"https://jbrowse.org/genomes/hg19/trix/hg19.ix\"\n", "ixx = \"https://jbrowse.org/genomes/hg19/trix/hg19.ixx\"\n", "meta = \"https://jbrowse.org/genomes/hg19/trix/meta.json\"\n", "conf_lgv.add_text_search_adapter(ix, ixx, meta)\n", "conf_lgv.set_default_session(['ncbi_gff_hg19'], False)\n", "conf_cgv.add_track(\"https://s3.amazonaws.com/jbrowse.org/genomes/hg19/reads_lr_skbr3.fa_ngmlr-0.2.3_mapped.bam.sniffles1kb_auto_l8_s5_noalt.vcf.gz\",name=\"SKBR3 pacbio (sniffles VCF)\", track_id=\"breast_cancer_sniffles_hg19\")\n", "conf_cgv.set_default_session([\"breast_cancer_sniffles_hg19\"], False)\n", "component_test = create_component(conf_cgv.get_config(), id=\"cgv-test\", dash_comp=\"CGV\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hyOYH1UjLdiS" }, "outputs": [], "source": [ "# Return the component with a specific location.\n", "@app.callback(\n", " Output(\"LGV-component\", \"children\"),\n", " Input(\"location-to-view\", \"value\")\n", ")\n", "def return_jbrowse(location):\n", " conf_lgv.set_location(location)\n", " return create_component(conf_lgv.get_config(), id=\"lgv-test\", dash_comp=\"LGV\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gTz7jhH7P8z8" }, "outputs": [], "source": [ "app.layout = html.Div(\n", " [\n", " html.P(\" SKBR3 Genes involved in fusions \"),\n", " dcc.Dropdown(\n", " id=\"location-to-view\", options=locations, value=\"1:229354402..229354403\", placeholder=\"Select a gene to view.\"\n", " ),\n", " html.Hr(),\n", " dcc.Loading(id=\"LGV-component\"),\n", " html.P(\"Circular Genome View with SKBR3 pacbio (sniffles VCF) track\"),\n", " component_test\n", " ]\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "rL8gnjaMQHeu", "outputId": "9758532d-fcdb-42c0-e527-61fb6c60eae8" }, "outputs": [ { "data": { "application/javascript": "(async (port, path, width, height, cache, element) => {\n if (!google.colab.kernel.accessAllowed && !cache) {\n return;\n }\n element.appendChild(document.createTextNode(''));\n const url = await google.colab.kernel.proxyPort(port, {cache});\n const iframe = document.createElement('iframe');\n iframe.src = new URL(path, url).toString();\n iframe.height = height;\n iframe.width = width;\n iframe.style.border = 0;\n element.appendChild(iframe);\n })(3031, \"/\", \"100%\", 1000, false, window.element)", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "app.run_server(mode=\"inline\", height=1000, use_reloader=False, debug=True, port=3031)\n" ] } ], "metadata": { "colab": { "authorship_tag": "ABX9TyOMAEpCwyMSE0fN/WR6igCI", "collapsed_sections": [], "include_colab_link": true, "name": "skbr3.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }