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Systems Genomics and Bioinformatics Unit · Laboratory of Systems Biology · National Institute of Allergy and Infectious Diseases
OMics Compendia Commons (OMiCC)

Welcome to OMics Compendia Commons (OMiCC)

Update log:

Dec 2020: The database has been synchronized with GEO to June 2020

Jul 2019: The database has been updated with GEO to January 1st 2019. Recount2 RNA-seq datasets have been added to OMiCC

Oct 2017: The database has been synchronized with GEO to April 2017. Also the Restful API support has been added

OMiCC is a community-based, biologist-friendly web platform for creating and (meta-) analyzing annotated gene-expression data compendia across studies and technology platforms for more than 40,000 human and mouse studies from Gene Expression Omnibus (GEO) and RNA-seq studies exported from recount2. An important feature of OMiCC is that it allows users to contribute to the community by sharing meta-data essential for data collation, reuse, and (meta-) analysis across studies. Thus, users of OMiCC can reuse sample groupings and pairings created by other users to construct cross-study data sets. We envision that as more users take advantage of OMiCC to perform biological hypothesis generation and discovery, more users will create and share such meta-data as well as data compendia and analyses with the biomedical research community.

Try our "Take a Tour" feature for more information about the page you are on. For detail introduction to OMiCC, go to the tutorial page

If you use OMiCC in your work, please cite:

Shah N*, Guo Y*, Wendelsdroff KV*, Lu Y, Sparks R, and Tsang JS. "A crowdsourcing approach for reusing and meta-analyzing gene expression data across studies and platforms". Nature Biotechnology 2016. DOI: 10.1038/nbt.3603 (* equal contributing authors)


NIH Jamboree 2016: Expanding the Immunology Toolbox

More information about this jamboree can be found in the following publication:

  • Sparks, R., Lau, W.W., and Tsang, J.S. (2016). Expanding the Immunology Toolbox: Embracing Public-Data Reuse and Crowdsourcing. Immunity 45, 1191–1204. DOI: 10.1016/j.immuni.2016.12.008
  • Lau, W.W., Sparks, R., OMiCC Jamboree Working Group, and Tsang, J.S. (2016). Meta-analysis of crowdsourced data compendia suggests pan-disease transcriptional signatures of autoimmunity. F1000Research 5, 2884. DOI: 10.12688/f1000research.10465.1