May 29, 2002

Accuracy and power of bayes prediction of amino acid sites under positive selection

Molecular Biology and Evolution
Maria AnisimovaZiheng Yang


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 (d(N)) than synonymous (d(S)) substitution rates or by omega = d(N)/d(S) > 1. Parameters were estimated by maximum likelihood under a codon substitution model that assumed several classes of sites with different omega 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 unacceptably low when data contained only a few highly similar sequences. However, sampling a large number of lineages i...Continue Reading

  • References23
  • Citations277


  • References23
  • Citations277

Mentioned in this Paper

Amino Acids, I.V. solution additive
Staphylococcal Protein A
Codon Genus
Codon (Nucleotide Sequence)
Bayesian Prediction
Positive Selection
Amino Acid [EPC]
Posterior Pituitary Disease
Evolution, Molecular

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