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Mol. Biol. Evol. 19(6):950958. 2002 2002 by the Society for Molecular Biology and Evolution. ISSN: 0737-4038
 

Summary: 950
Mol. Biol. Evol. 19(6):950­958. 2002
2002 by the Society for Molecular Biology and Evolution. ISSN: 0737-4038
Accuracy and Power of Bayes Prediction of Amino Acid Sites Under
Positive Selection
Maria Anisimova,* Joseph P. Bielawski,* and Ziheng Yang*
*Department of Biology, Galton Laboratory and Center for Mathematics and Physics in the Life Sciences and Experimental
Biology (CoMPLEX), University College London
Bayes prediction quantifies uncertainty by assigning posterior probabilities. It was used to identify amino acids in
a protein under recurrent diversifying selection indicated by higher nonsynonymous (dN) than synonymous (dS)
substitution rates or by dN/dS 1. Parameters were estimated by maximum likelihood under a codon substi-
tution model that assumed several classes of sites with different ratios. The Bayes theorem was used to calculate
the posterior probabilities of each site falling into these site classes. Here, we evaluate the performance of Bayes
prediction of amino acids under positive selection by computer simulation. We measured the accuracy by the
proportion of predicted sites that were truly under selection and the power by the proportion of true positively
selected sites that were predicted by the method. The accuracy was slightly better for longer sequences, whereas
the power was largely unaffected by the increase in sequence length. Both accuracy and power were higher for
medium or highly diverged sequences than for similar sequences. We found that accuracy and power were unac-
ceptably low when data contained only a few highly similar sequences. However, sampling a large number of
lineages improved the performance substantially. Even for very similar sequences, accuracy and power can be high

  

Source: Anisimova, Maria - Institute of Scientific Computing, Eidgenössische Technische Hochschule Zürich (ETHZ)
Yang, Ziheng - Department of Genetics, Evolution and Environment, University College London

 

Collections: Biology and Medicine; Environmental Sciences and Ecology