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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:

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

October 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 24,000 human and mouse studies from Gene Expression Omnibus (GEO). 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