Abstract
BackgroundThe covarion hypothesis of molecular evolution holds that selective pressures on a given amino acid or nucleotide site are dependent on the identity of other sites in the molecule that change throughout time, resulting in changes of evolutionary rates of sites along the branches of a phylogenetic tree. At the sequence level, covarion-like evolution at a site manifests as conservation of nucleotide or amino acid states among some homologs where the states are not conserved in other homologs (or groups of homologs). Covarion-like evolution has been shown to relate to changes in functions at sites in different clades, and, if ignored, can adversely affect the accuracy of phylogenetic inference.ResultsPROCOV (protein covarion analysis) is a software tool that implements a number of previously proposed covarion models of protein evolution for phylogenetic inference in a maximum likelihood framework. Several algorithmic and implementation improvements in this tool over previous versions make computationally expensive tree searches with covarion models more efficient and analyses of large phylogenomic data sets tractable. PROCOV can be used to identify covarion sites by comparing the site likelihoods under the covarion process to the corresponding site likelihoods under a rates-across-sites (RAS) process. Those sites with the greatest log-likelihood difference between a 'covarion' and an RAS process were found to be of functional or structural significance in a dataset of bacterial and eukaryotic elongation factors.ConclusionCovarion models implemented in PROCOV may be especially useful for phylogenetic estimation when ancient divergences between sequences have occurred and rates of evolution at sites are likely to have changed over the tree. It can also be used to study lineage-specific functional shifts in protein families that result in changes in the patterns of site variability among subtrees.
Highlights
The covarion hypothesis of molecular evolution proposes that selective pressures on a given amino acid or nucleotide site are dependent on the identity of other sites in the molecule that change throughout time, resulting in changes of evolutionary rates of sites along the branches of a phylogenetic tree [1]
Comparing the speedup of PROCOV with the new Basic Linear Algebra Subprograms (BLAS) implementation To compare the speedup of PROCOV with the BLAS implementation versus the non-BLAS implementation, http://www.biomedcentral.com/1471-2148/9/225 we tested two protein datasets (Acetyl-CoA carboxylase with 36 taxa and 212 sites and Heat shock protein 70 (HSP70) with 34 taxa and 432 sites) for fixed topologies, previously inferred with PHYML [41] under JTT + Gamma, and optimized the parameters with JTT + the general covarion model with PROCOV
The final likelihood scores yielded by the BLAS and non-BLAS versions of PROCOV are the same in both cases
Summary
The covarion hypothesis of molecular evolution holds that selective pressures on a given amino acid or nucleotide site are dependent on the identity of other sites in the molecule that change throughout time, resulting in changes of evolutionary rates of sites along the branches of a phylogenetic tree. Covarion-like evolution is widely recognized as an important mode of molecular evolution in protein-coding genes, structural RNA, and DNA regulatory elements (e.g., [4,5,6]) This realization has fueled the development of several kinds of phylogenetic models including: (i) 'covarion models' that are designed to model the stochastic changes of rates at sites in sequences [7,8,9,10,11,12], (ii) discrete 'rate-shift' models where sudden changes in rates at multiple sites occur at particular splits in the tree [13], and (iii) mixture of branch lengths-based heterotachy models [14,15,16,17,18]. Other studies have focused on developing computational methods to detect whether covarion-like evolution occurs in protein families [20,21,22], identify covarion or heterotachous sites to analyse functional shifts in a protein family [2,13,23,24,25,26,27,28] and detect positive selection in primate and viral genes [28,29,30,31]
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