These tracks are miRNA expression levels of 196 primary cell types. Data was obtained from multiple sources (2,406 samples) corresponding to 175 unique projects. Few of the major sources are from McCall MN. et al., cells were generally grown in culture or were flow sorted; Rie D. D. et al., FANTOM5 data, which was obtained from commercially purchased primary cell lines grown in culture; Juzenas S. et al., the isolation technique was magnetic activated cell sorting (MACS) from total blood; Lorenzi L. et al., RNA atlas project, RNA of individual cell types was obtained from ScienCell Research Laboratories or isolated from cell types collected at Ghent University Hospital; etc.,

The reads per million (RPM) values obtained from these samples representing respective cell types were used to create the barChart data. The data is not log normalized, althogh not part of the display, several normalization methods were tested and "DESeq2 VST", was considered the best among them. More details of data processing can be found in the Methods.

Summary of cell-class used in this analysis

The cell types originated from multiple sources in the body are broadly classified as cell-class, for examples cell types such as retinal pigment, beta cells, colonic epithelial cells etc. are classified under cell-class "Epithelial". Further Plasma and Platelets are not cell types by itself, however, they are annotated/termed as such for the analysis and data representation. The color code displays the color assigned to each cell type in the box plot and barChart representations.

Color code Class Number of samples Number of cell types
Immune 839 31
Platelet 25 1
Plasma 139 1
RBC 85 2
Fibroblast 121 32
Muscle 124 24
Fat 19 3
Epithelial 274 36
Endothelial 174 14
Brain 80 3
Other 39 15
Sperm 95 1
Stem 392 35
Total 2406 198

Display Conventions and Configuration

Samples are color coded, using the GTEx color palate to indicate similar cell types. Individual cells can be toggled on/off using the "Go to Primary cells updated track controls" tool. The track is best viewed with the Log10(x+1)transform unselected and the view limits maximum set to 50,000 RPM or similar.


The data in FASTQ format were downloaded from the NCBI-Sequence Read Archive using fastq-dump or fasterq-dump. The data were processed for quality filter steps and subsequently processed with miRge3.0 for miRNA annotation and miRNAs were normalized to reads per million miRNA reads (RPM).


The data is devoid of any samples corresponding to any of the following scenario: Exosomes, Cancer cells, reads with both end adapters and 5' adapters, reads with barcodes, reads with Unique Molecular Identifiers (UMI) etc. Furthermore, only data that were sequenced with Illumina sequencing platform was considered for the analysis. For a full description of the method, please see the methods section of the manuscript below (Patil et al 2022).

Data Access

All primary data is available through the Sequence Read Archive. Specific sample information can be obtained through the "cellular microRNAome" manuscript listed below.


Arun H. Patil, Andrea Baran, Zach Brehm, Matthew N. McCall, Marc K. Halushka.

Arun and Marc thank the FANTOM5 team and the Hemmrich-Stanisak laboratory for making their data available in the public domain and the UCSC Genome Browser team for excellent technical assistance.


For inquiries, please contact Marc Halushka


1. Arun H. Patil, et al. A curated human cellular microRNAome based on 196 primary cell types. Under revision. 2022.

2. Matthew N. McCall, et al. Towards the human cellular microRNAome. Genome Research, 2017.

3. Derek de Rie, et al. An integrated expression atlas of miRNAs and their promoters in human and mouse Nature Biotechnology, 2017.

4. Simonas Juzenas et al. A comprehensive, cell specific microRNA catalogue of human peripheral blood Nucleic Acids Research, 2017.

5. Lucia Lorenzi et al. The RNA Atlas expands the catalog of human non-coding RNAs. Nature biotechnology. 2021.

6. Arun H. Patil and Marc K. Halushka. miRge3.0: a comprehensive microRNA and tRF sequencing analysis pipeline. NAR Genomics and Bioinformatics. 2021.