Wang, Zichen and Monteiro, Caroline D. and Jagodnik, Kathleen M. and Fernandez, Nicolas F. and Gundersen, Gregory W. and Rouillard, Andrew D. and Jenkins, Sherry L. and Feldmann, Axel S. and Hu, Kevin S. and McDermott, Michael G. and Duan, Qiaonan and Clark, Neil R. and Jones, Matthew R. and Kou, Yan and Goff, Troy and Woodland, Holly and Amaral, Fabio M. R. and Szeto, Gregory L. and Fuchs, Oliver and Rose, Sophia M. Schussler-Fiorenza and Sharma, Shvetank and Schwartz, Uwe and Bengoetxea Bausela, Xabier and Szymkiewicz, Maciej and Maroulis, Vasileios and Salykin, Anton and Barra, Carolina M. and Kruth, Candice D. and Bongio, Nicholas J. and Mathur, Vaibhav and Todoric, Radmila D. and Rubin, Udi E. and Malatras, Apostolos and Fulp, Carl T. and Galindo, John A. and Motiejunaite, Ruta and Jueschke, Christoph and Dishuck, Philip C. and Lahl, Katharina and Jafari, Mohieddin and Aibar, Sara and Zaravinos, Apostolos and Steenhuizen, Linda H. and Allison, Lindsey R. and Gamallo, Pablo and de Andres Segura, Fernando and Devlin, Tyler Dae and Perez-Garcia, Vicente and Ma'ayan, Avi (2016) Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. NATURE COMMUNICATIONS, 7: 12846. ISSN 2041-1723,
Full text not available from this repository. (Request a copy)Abstract
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 associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | FACIOSCAPULOHUMERAL MUSCULAR-DYSTROPHY; ENDOMETRIAL CANCER-RISK; ESTROGEN-RECEPTOR; HEPATOCELLULAR-CARCINOMA; DIFFERENTIAL EXPRESSION; DATABASE; DISEASE; DISCOVERY; INSULIN; GROWTH; |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biochemie, Genetik und Mikrobiologie > Lehrstuhl für Biochemie III |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 04 Apr 2019 11:31 |
| Last Modified: | 04 Apr 2019 11:31 |
| URI: | https://pred.uni-regensburg.de/id/eprint/3375 |
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