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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
biochemistry, genetics and molecular biology
Robust inference of positive selection from recombining coding sequences
Bioinformatics, Volume 22, No. 20, Year 2006
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Description
Motivation: Accurate detection of positive Darwinian selection can provide important insights to researchers investigating the evolution of pathogens. However, many pathogens (particularly viruses) undergo frequent recombination and the phylogenetic methods commonly applied to detect positive selection have been shown to give misleading results when applied to recombining sequences. We propose a method that makes maximum likelihood inference of positive selection robust to the presence of recombination. This is achieved by allowing tree topologies and branch lengths to change across detected recombination breakpoints. Further improvements are obtained by allowing synonymous substitution rates to vary across sites. Results: Using simulation we show that, even for extreme cases where recombination causes standard methods to reach false positive rates >90%, the proposed method decreases the false positive rate to acceptable levels while retaining high power. We applied the method to two HIV-1 datasets for which we have previously found that inference of positive selection is invalid owing to high rates of recombination. In one of these (env gene) we still detected positive selection using the proposed method, while in the other (gag gene) we found no significant evidence of positive selection. © 2006 Oxford University Press.
Authors & Co-Authors
Scheffler, Konrad
South Africa, Cape Town
University of Cape Town
Martin, Darren Patrick
South Africa, Cape Town
University of Cape Town
Seoighe, Cathal
South Africa, Cape Town
University of Cape Town
Statistics
Citations: 199
Authors: 3
Affiliations: 1
Identifiers
Doi:
10.1093/bioinformatics/btl427
ISSN:
13674803
e-ISSN:
13674811
Research Areas
Genetics And Genomics
Infectious Diseases