Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd
- Wang, Zichen
- Monteiro, Caroline D.
- Jagodnik, Kathleen M.
- Fernandez, Nicolas F.
- Gundersen, Gregory W.
- Rouillard, Andrew D.
- Jenkins, Sherry L.
- Feldmann, Axel S.
- Hu, Kevin S.
- McDermott, Michael G.
- Duan, Qiaonan
- Clark, Neil R.
- Jones, Matthew R.
- Kou, Yan
- Goff, Troy
- Woodland, Holly
- Amaral, Fabio M. R.
- Szeto, Gregory L.
- Fuchs, Oliver
- Rose Schüssler-Fiorenza, Sophia M.
- Sharma, Shvetank
- Schwartz, Uwe
- Bengoetxea Bausela, Xabier
- Szymkiewicz, Maciej
- Maroulis, Vasileios
- Salykin, Anton
- Barra, Carolina M.
- Kruth, Candice D.
- Bongio, Nicholas J.
- Mathur, Vaibhav
- Todoric, Radmila D.
- Rubin, Udi E.
- Malatras, Apostolos
- Fulp, Carl T.
- Galindo, John A.
- Motiejunaite, Ruta
- Jüschke, Christoph
- Dishuck, Philip C.
- Lahl, Katharina
- Jafari, Mohieddin
- Aibar, Sara
- Zaravinos, Apostolos
- Steenhuizen, Linda H.
- Allison, Lindsey R.
- Gamallo, Pablo
- de Andres Segura, Fernando
- Devlin, Tyler Dae
- Perez-Garcia, Vicente
- Ma'ayan, Avi
DOIAbstract
Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associa...
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