GeNets: a unified web platform for network-based genomic analyses
Taibo Li(Broad Institute), April Kim(Broad Institute), Joseph Rosenbluh(Broad Institute), Heiko Horn(Broad Institute), Liraz Greenfeld(Broad Institute), David An(Broad Institute), Andrew Zimmer(Broad Institute), Arthur Liberzon(Broad Institute), Jon Bistline(Broad Institute), Ted Natoli(Broad Institute), Yang Li(Broad Institute), Aviad Tsherniak(Broad Institute), Rajiv Narayan(Broad Institute), Aravind Subramanian(Broad Institute), Ted Liefeld(Broad Institute), Bang Wong(Broad Institute), Dawn Thompson(Broad Institute), Sarah E. Calvo(Broad Institute), Steve Carr(Broad Institute), Jesse S. Boehm(Broad Institute), Jake Jaffe(Broad Institute), Jill P. Mesirov(Broad Institute), Nir Hacohen(Broad Institute), Aviv Regev(Broad Institute), Kasper Lage(Broad Institute)
Cited by 61Open Access
Abstract
Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to compare the signal-to-noise ratios of different networks and to identify the optimal network with which to interpret a particular genetic dataset. We present GeNets, a platform in which users can train a machine-learning model (Quack) to carry out these comparisons and execute, store, and share analyses of genetic and RNA-sequencing datasets. The GeNets web platform can identify the most informative network, as well as execute, store and share network-based analyses of RNA-seq or genomic datasets.
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