Likelihood-based clustering (LiBaC) for codon models

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter focuses on the likelihood-based clustering (LiBaC) method and its application to models of codon evolution. The LiBaC method for partitioning sites into groups, or 'clusters', where each group has a different model, was developed by Bao et al. (2008). LiBaC can provide reliable parameter estimates under an appropriate model when the process of evolution is very heterogeneous among groups of sites, and it can be used to identify sites subject to positive selection. LiBaC can be employed to search for genes having evolved under positive selection pressure. This chapter presents a large-scale survey of genes encoding transmembrane proteins-a hallmark of these genes is substantial evolutionary heterogeneity among sites. It also reviews the conclusions derived by Bao et al. (2008) from their simulation studies, and examines the effect of different posterior probability cutoffs on LiBaC performance.

Original languageEnglish
Title of host publicationCodon Evolution
Subtitle of host publicationMechanisms and Models
PublisherOxford University Press
ISBN (Electronic)9780191810114
ISBN (Print)9780199601165
DOIs
Publication statusPublished - Feb 23 2012

Bibliographical note

Publisher Copyright:
© Oxford University Press, 2015.

ASJC Scopus Subject Areas

  • General Agricultural and Biological Sciences

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