Cross-species analysis of biological networks by Bayesian alignment
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Edited by Michael S. Waterman, University of Southern California, Los Angeles, CA, and approved May 22, 2006 (received for review March 27, 2006)
Abstract
Complex interactions between genes or proteins contribute a substantial part to phenotypic evolution. Here we develop an evolutionarily grounded method for the cross-species analysis of interaction networks by alignment, which maps bona fide functional relationships between genes in different organisms. Network alignment is based on a scoring function measuring mutual similarities between networks, taking into account their interaction patterns as well as sequence similarities between their nodes. High-scoring alignments and optimal alignment parameters are inferred by a systematic Bayesian analysis. We apply this method to analyze the evolution of coexpression networks between humans and mice. We find evidence for significant conservation of gene expression clusters and give network-based predictions of gene function. We discuss examples where cross-species functional relationships between genes do not concur with sequence similarity.
Footnotes
- †To whom correspondence should be addressed. E-mail: berg@thp.uni-koeln.de
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Author contributions: J.B. and M.L. designed research, performed research, analyzed data, and wrote the paper.
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Conflict of interest statement: No conflicts declared.
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This paper was submitted directly (Track II) to the PNAS office.
- © 2006 by The National Academy of Sciences of the USA