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Open Access Highly Accessed Research article

Detection of lineage-specific evolutionary changes among primate species

Mihaela Pertea1*, Geo M Pertea1 and Steven L Salzberg2

Author Affiliations

1 Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA

2 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

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BMC Bioinformatics 2011, 12:274  doi:10.1186/1471-2105-12-274

Published: 4 July 2011

Abstract

Background

Comparison of the human genome with other primates offers the opportunity to detect evolutionary events that created the diverse phenotypes among the primate species. Because the primate genomes are highly similar to one another, methods developed for analysis of more divergent species do not always detect signs of evolutionary selection.

Results

We have developed a new method, called DivE, specifically designed to find regions that have evolved either more or less rapidly than expected, for any clade within a set of very closely related species. Unlike some previous methods, DivE does not rely on rates of synonymous and nonsynonymous substitution, which enables it to detect evolutionary events in noncoding regions. We demonstrate using simulated data that DivE compares favorably to alternative methods, and we then apply DivE to the ENCODE regions in 14 primate species. We identify thousands of regions in these primates, ranging from 50 to >10000 bp in length, that appear to have experienced either constrained or accelerated rates of evolution. In particular, we detected 4942 regions that have potentially undergone positive selection in one or more primate species. Most of these regions occur outside of protein-coding genes, although we identified 20 proteins that have experienced positive selection.

Conclusions

DivE provides an easy-to-use method to predict both positive and negative selection in noncoding DNA, that is particularly well-suited to detecting lineage-specific selection in large genomes.