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Mol Biol Evol. 2015 May;32(5):1365-71. doi: 10.1093/molbev/msv035. Epub 2015 Feb 19.

Gene-wide identification of episodic selection.

Author information

1
Department of Medicine, University of California San Diego.
2
Graduate program in Bioinformatics and Systems Biology, University of California San Diego.
3
Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA.
4
Graduate program in Bioinformatics and Systems Biology, University of California San Diego Graduate program in Biomedical Informatics, University of California San Diego.
5
Canyon Crest Academy, San Diego, CA.
6
Computational Biology Group, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa.
7
Department of Medicine, University of California San Diego Veterans Affairs San Diego Healthcare System, San Diego, CA.
8
Department of Medicine, University of California San Diego Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa.
9
Department of Medicine, University of California San Diego spond@ucsd.edu.

Abstract

We present BUSTED, a new approach to identifying gene-wide evidence of episodic positive selection, where the non-synonymous substitution rate is transiently greater than the synonymous rate. BUSTED can be used either on an entire phylogeny (without requiring an a priori hypothesis regarding which branches are under positive selection) or on a pre-specified subset of foreground lineages (if a suitable a priori hypothesis is available). Selection is modeled as varying stochastically over branches and sites, and we propose a computationally inexpensive evidence metric for identifying sites subject to episodic positive selection on any foreground branches. We compare BUSTED with existing models on simulated and empirical data. An implementation is available on www.datamonkey.org/busted, with a widget allowing the interactive specification of foreground branches.

KEYWORDS:

branch-site model; episodic selection; evolutionary model; random effects model

PMID:
25701167
PMCID:
PMC4408417
DOI:
10.1093/molbev/msv035
[Indexed for MEDLINE]
Free PMC Article

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