Bayes Empirical Bayes Inference of Amino Acid Sites Under Positive Selection Export

@article{citeulike:147383, abstract = {Codon-based substitution models have been widely used to identify amino acid sites under positive selection in comparative analysis of protein-coding DNA sequences. The nonsynonymous-synonymous substitution rate ratio (d(N)/d(S), denoted omega) is used as a measure of selective pressure at the protein level, with omega > 1 indicating positive selection. Statistical distributions are used to model the variation in omega among sites, allowing a subset of sites to have omega > 1 while the rest of the sequence may be under purifying selection with omega < 1. An empirical Bayes (EB) approach is then used to calculate posterior probabilities that a site comes from the site class with omega > 1. Current implementations, however, use the naive EB (NEB) approach and fail to account for sampling errors in maximum likelihood estimates of model parameters, such as the proportions and omega ratios for the site classes. In small data sets lacking information, this approach may lead to unreliable posterior probability calculations. In this paper, we develop a Bayes empirical Bayes (BEB) approach to the problem, which assigns a prior to the model parameters and integrates over their uncertainties. We compare the new and old methods on real and simulated data sets. The results suggest that in small data sets the new BEB method does not generate false positives as did the old NEB approach, while in large data sets it retains the good power of the NEB approach for inferring positively selected sites.}, author = {Yang, Ziheng and Wong, Wendy S. and Nielsen, Rasmus}, citeulike-article-id = {147383}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/molbev/msi097}, citeulike-linkout-1 = {http://mbe.oxfordjournals.org/cgi/content/abstract/22/4/1107}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/oup/molbev/2005/00000022/00000004/art01107}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/15689528}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=15689528}, doi = {10.1093/molbev/msi097}, issn = {0737-4038}, journal = {Molecular Biology and Evolution}, keywords = {acid, amino, bayes, empirical, molevoltestis, positive, selection}, month = {April}, number = {4}, pages = {1107--1118}, posted-at = {2008-06-06 16:23:44}, priority = {0}, publisher = {Oxford University Press}, title = {Bayes Empirical Bayes Inference of Amino Acid Sites Under Positive Selection}, url = {http://dx.doi.org/10.1093/molbev/msi097}, volume = {22}, year = {2005} }

TY - JOUR ID - citeulike:147383 L3 - citeulike-article-id:147383 N2 - Codon-based substitution models have been widely used to identify amino acid sites under positive selection in comparative analysis of protein-coding DNA sequences. The nonsynonymous-synonymous substitution rate ratio (d(N)/d(S), denoted omega) is used as a measure of selective pressure at the protein level, with omega > 1 indicating positive selection. Statistical distributions are used to model the variation in omega among sites, allowing a subset of sites to have omega > 1 while the rest of the sequence may be under purifying selection with omega < 1. An empirical Bayes (EB) approach is then used to calculate posterior probabilities that a site comes from the site class with omega > 1. Current implementations, however, use the naive EB (NEB) approach and fail to account for sampling errors in maximum likelihood estimates of model parameters, such as the proportions and omega ratios for the site classes. In small data sets lacking information, this approach may lead to unreliable posterior probability calculations. In this paper, we develop a Bayes empirical Bayes (BEB) approach to the problem, which assigns a prior to the model parameters and integrates over their uncertainties. We compare the new and old methods on real and simulated data sets. The results suggest that in small data sets the new BEB method does not generate false positives as did the old NEB approach, while in large data sets it retains the good power of the NEB approach for inferring positively selected sites. IS - 4 JF - Molecular Biology and Evolution SN - 0737-4038 EP - 1118 TI - Bayes Empirical Bayes Inference of Amino Acid Sites Under Positive Selection PB - Oxford University Press VL - 22 SP - 1107 KW - acid KW - amino KW - bayes KW - empirical KW - molevoltestis KW - positive KW - selection AU - Yang, Ziheng AU - Wong, Wendy AU - Nielsen, Rasmus PY - 2005/04// UR - http://dx.doi.org/10.1093/molbev/msi097 ER -