Wolfe Nicholas W, Clark Nathan L
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Bioinformatics. 2015 Dec 1;31(23):3835-7. doi: 10.1093/bioinformatics/btv454. Epub 2015 Aug 4.
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.
Analyses and data at http://csb.pitt.edu/erc_analysis/
近期比较基因组学数据的激增为通过进化速率共变(ERC)特征构建基因网络提供了前所未有的机遇。ERC用于识别经历相似进化历史的基因,从而推断它们之间的功能关联。ERC分析网站允许研究人员利用全基因组数据集推断任何生物学功能中的新基因,并探索不同途径和复合物之间深层次的进化联系。该网站提供五种分析方法、图形输出、统计支持,并可访问越来越多的分类群。
分析和数据可在http://csb.pitt.edu/erc_analysis/获取