Likelihood Based Clustering (LiBaC) for Codon Models, a method for grouping sites according to similarities in the underlying process of evolution. Export

@article{citeulike:3063929, abstract = {Models of codon evolution are useful for investigating the strength and direction of natural selection via a parameter for the nonsynonymous/synonymous rate ratio (omega = d(N) /d(S)). Different codon models are available to account for diversity of the evolutionary patterns among sites. Codon models which specify data partitions as fixed effects allow the most evolutionary diversity among sites, but require that sites-partitions are a priori identifiable. Models which use a parametric distribution to express the variability in the omega ratio across sites do not require a priori partitioning of sites, but they permit less among-site diversity in the evolutionary process. Simulation studies presented in this paper indicate that differences among sites in estimates of omega under an overly simplistic analytical model can reflect more than just natural selection pressure. We also find that the classic LRTs for positive selection have a high false positive rate in some situations. In this paper we developed a new method for assigning codon sites into groups where each group has a different model, and the likelihood over all sites is maximized. The method, called Likelihood Based Clustering (LiBaC), can be viewed as a generalization of the family of Model Based Clustering (MBC) approaches to models of codon evolution. We report the performance of several LiBaC-based methods, and selected alternative methods, over a wide variety of scenarios. We find that LiBaC, under an appropriate model, can provide reliable parameter estimates when the process of evolution is very heterogeneous among groups of sites. Certain types of proteins, such as transmembrane proteins, are expected to exhibit such heterogeneity. A survey of genes encoding transmembrane proteins suggests that overly-simplistic models could be leading to false signal for positive selection among such genes. In these cases, LiBaC-based methods offer an important addition to a "tool box" of methods thereby helping to uncover robust evidence for the action of positive selection.}, address = {Department of Mathematics and Statistics, Dalhousie University, Halifax Nova Scotia, B3H 4J1, Canada.}, author = {Bao, Le and Gu, Hong and Dunn, Katherine A A. and Bielawski, Joseph P P.}, citeulike-article-id = {3063929}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/molbev/msn145}, citeulike-linkout-1 = {http://mbe.oxfordjournals.org/cgi/content/abstract/25/9/1995}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18586695}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18586695}, day = {26}, doi = {10.1093/molbev/msn145}, issn = {1537-1719}, journal = {Molecular biology and evolution}, keywords = {evolution, natural, protein, selection}, month = {June}, posted-at = {2008-11-18 04:53:48}, priority = {0}, title = {Likelihood Based Clustering (LiBaC) for Codon Models, a method for grouping sites according to similarities in the underlying process of evolution.}, url = {http://dx.doi.org/10.1093/molbev/msn145}, year = {2008} }

TY - JOUR ID - citeulike:3063929 L3 - citeulike-article-id:3063929 N2 - Models of codon evolution are useful for investigating the strength and direction of natural selection via a parameter for the nonsynonymous/synonymous rate ratio (omega = d(N) /d(S)). Different codon models are available to account for diversity of the evolutionary patterns among sites. Codon models which specify data partitions as fixed effects allow the most evolutionary diversity among sites, but require that sites-partitions are a priori identifiable. Models which use a parametric distribution to express the variability in the omega ratio across sites do not require a priori partitioning of sites, but they permit less among-site diversity in the evolutionary process. Simulation studies presented in this paper indicate that differences among sites in estimates of omega under an overly simplistic analytical model can reflect more than just natural selection pressure. We also find that the classic LRTs for positive selection have a high false positive rate in some situations. In this paper we developed a new method for assigning codon sites into groups where each group has a different model, and the likelihood over all sites is maximized. The method, called Likelihood Based Clustering (LiBaC), can be viewed as a generalization of the family of Model Based Clustering (MBC) approaches to models of codon evolution. We report the performance of several LiBaC-based methods, and selected alternative methods, over a wide variety of scenarios. We find that LiBaC, under an appropriate model, can provide reliable parameter estimates when the process of evolution is very heterogeneous among groups of sites. Certain types of proteins, such as transmembrane proteins, are expected to exhibit such heterogeneity. A survey of genes encoding transmembrane proteins suggests that overly-simplistic models could be leading to false signal for positive selection among such genes. In these cases, LiBaC-based methods offer an important addition to a "tool box" of methods thereby helping to uncover robust evidence for the action of positive selection. JF - Molecular biology and evolution SN - 1537-1719 AD - Department of Mathematics and Statistics, Dalhousie University, Halifax Nova Scotia, B3H 4J1, Canada. TI - Likelihood Based Clustering (LiBaC) for Codon Models, a method for grouping sites according to similarities in the underlying process of evolution. KW - evolution KW - natural KW - protein KW - selection AU - Bao, Le AU - Gu, Hong AU - Dunn, Katherine A AU - Bielawski, Joseph P PY - 2008/06/26/ UR - http://dx.doi.org/10.1093/molbev/msn145 ER -