GeNets: A unified web platform for network-based analyses of genomic data

Taibo Li; April Kim; Joseph Rosenbluh; Heiko Horn; Liraz Greenfeld; David An; Andrew R. Zimmer; Arthur Liberzon; Jon Bistline; Ted Natoli; Yang Li; Aviad Tsherniak; Rajiv Narayan; Aravind Subramanian; Ted Liefeld; Bang Wong; Dawn Anne Thompson; Sarah E. Calvo; Steven A. Carr; Jesse S. Boehm; Jacob D. Jaffe; Jill P. Mesirov; Nir Hacohen; Aviv Regev; Kasper Lage

doi:10.1038/s41592-018-0039-6

GenomicsNature MethodsJun 15, 2018

Peer-reviewed journal article

GeNets: A unified web platform for network-based analyses of genomic data

Taibo Li iD, April Kim iD, Joseph Rosenbluh iD, Heiko Horn iD, Liraz Greenfeld, David An, Andrew R. Zimmer, Arthur Liberzon, Jon Bistline, Ted Natoli, Yang Li, Aviad Tsherniak iD, Rajiv Narayan, Aravind Subramanian iD, Ted Liefeld iD, Bang Wong, Dawn Anne Thompson iD, Sarah E. Calvo iD, Steven A. Carr iD, Jesse S. Boehm iD, Jacob D. Jaffe iD, Jill P. Mesirov iD, Nir Hacohen iD, Aviv Regev iD, Kasper Lage iD

Abstract

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.

PDF DOI