Precision annotation of digital samples in NCBI's gene expression omnibus

Dexter Hadley, James Pan, Osama El-Sayed, Jihad Aljabban, Imad Aljabban, Tej D. Azad, Mohamad O. Hadied, Shuaib Raza, Benjamin Abhishek Rayikanti, Bin Chen, Hyojung Paik, Dvir Aran (+7 others)
2017 Scientific Data  
The Gene Expression Omnibus (GEO) contains more than two million digital samples from functional genomics experiments amassed over almost two decades. However, individual sample meta-data remains poorly described by unstructured free text attributes preventing its largescale reanalysis. We introduce the Search Tag Analyze Resource for GEO as a web application (http://STARGEO.org) to curate better annotations of sample phenotypes uniformly across different studies, and to use these sample
more » ... ions to define robust genomic signatures of disease pathology by meta-analysis. In this paper, we target a small group of biomedical graduate students to show rapid crowd-curation of precise sample annotations across all phenotypes, and we demonstrate the biological validity of these crowd-curated annotations for breast cancer. STARGEO.org makes GEO data findable, accessible, interoperable and reusable (i.e., FAIR) to ultimately facilitate knowledge discovery. Our work demonstrates the utility of crowd-curation and interpretation of open 'big data' under FAIR principles as a first step towards realizing an ideal paradigm of precision medicine.
doi:10.1038/sdata.2017.125 pmid:28925997 pmcid:PMC5604135 fatcat:2bzcq5qvzfe3ffob3jhxniitem