biostudies-literatureHadley DNIAID NIH HHSNLM NIH HHSNCI NIH HHSNIAMS NIH HHSNIGMS NIH HHS170125https://www.ebi.ac.uk/biostudies/studies/S-EPMC5604135biostudies-literatureUnknown4The 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 annotations 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.Scientific dataPrecision annotation of digital samples in NCBI's gene expression omnibus.PMC5604135R01 GM079719P30 AR070155U01 LM012675HHSN272201200028CUH2 CA203792Spatz JPanahiazar MBhattacharya SSirota MRayikanti BAChen BHadley DHadied MOEl-Sayed OPaik HPan JAljabban JMusen MAAljabban IRaza SAzad TDAran DButte AJHimmelstein DfalsePrecision annotation of digital samples in NCBI's gene expression omnibus.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 annotations 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.2017-01-01T00:00:00Z2017 Sep2024-10-18T11:59:41.883Z2019-03-27T02:56:46ZS-EPMC56041352892599710.1038/sdata.2017.125