ERC analysis: web-based inference of gene function via evolutionary rate covariation | Zendy

Nicholas W. Wolfe | Zendy; Nathan L. Clark | Zendy

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ERC analysis: web-based inference of gene function via evolutionary rate covariation

Author(s) -

Nicholas W. Wolfe,

Nathan L. Clark

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv454

Subject(s) - inference , construct (python library) , function (biology) , genomics , exploit , statistical inference , comparative genomics , biology , computational biology , computer science , genome , gene , evolutionary biology , artificial intelligence , genetics , statistics , mathematics , computer security , programming language

The recent explosion of comparative genomics data presents an unprecedented opportunity to construct gene networks via the evolutionary rate covariation (ERC) signature. ERC is used to identify genes that experienced similar evolutionary histories, and thereby draws functional associations between them. The ERC Analysis website allows researchers to exploit genome-wide datasets to infer novel genes in any biological function and to explore deep evolutionary connections between distinct pathways and complexes. The website provides five analytical methods, graphical output, statistical support and access to an increasing number of taxonomic groups.

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