OMiCC: An expanded and enhanced platform for meta-analysis of public gene expression data

OMiCC (OMics Compendia Commons) is a biologist-friendly web platform that facilitates data reuse and integration. Users can search over 40,000 publicly available gene expression studies, annotate and curate samples, and perform meta-analysis. Since the initial publication, we have incorporated RNA-s...

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Veröffentlicht in:STAR protocols 2022-09, Vol.3 (3), p.101474-101474, Article 101474
Hauptverfasser: Liu, Candace C., Guo, Yongjian, Vrindten, Kiera L., Lau, William W., Sparks, Rachel, Tsang, John S.
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Sprache:eng
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Zusammenfassung:OMiCC (OMics Compendia Commons) is a biologist-friendly web platform that facilitates data reuse and integration. Users can search over 40,000 publicly available gene expression studies, annotate and curate samples, and perform meta-analysis. Since the initial publication, we have incorporated RNA-seq datasets, compendia sharing, RESTful API support, and an additional meta-analysis method based on random effects. Here, we provide a step-by-step guide for using OMiCC. For complete details on the use and execution of this protocol, please refer to Shah et al. (2016). [Display omitted] •OMiCC (OMics Compendia Commons) is a free web-based tool for gene expression data reuse•Search publicly available studies to perform sample group comparisons to explore a disease•In meta-analysis, multiple studies are combined to identify coherent signals•OMiCC supports crowd-sharing and users can share their own analyses with the community Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. OMiCC (OMics Compendia Commons) is a biologist-friendly web platform that facilitates data reuse and integration. Users can search over 40,000 publicly available gene expression studies, annotate and curate samples, and perform meta-analysis. Since the initial publication, we have incorporated RNA-seq datasets, compendia sharing, RESTful API support, and an additional meta-analysis method based on random effects. Here, we provide a step-by-step guide for using OMiCC.
ISSN:2666-1667
2666-1667
DOI:10.1016/j.xpro.2022.101474