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Detecting amino acid sites under positive selection and purifying selection.

ABSTRACT: An excess of nonsynonymous over synonymous substitution at individual amino acid sites is an important indicator that positive selection has affected the evolution of a protein between the extant sequences under study and their most recent common ancestor. Several methods exist to detect the presence, and sometimes location, of positively selected sites in alignments of protein-coding sequences. This article describes the "sitewise likelihood-ratio" (SLR) method for detecting nonneutral evolution, a statistical test that can identify sites that are unusually conserved as well as those that are unusually variable. We show that the SLR method can be more powerful than currently published methods for detecting the location of positive selection, especially in difficult cases where the strength of selection is low. The increase in power is achieved while relaxing assumptions about how the strength of selection varies over sites and without elevated rates of false-positive results that have been reported with some other methods. We also show that the SLR method performs well even under circumstances where the results from some previous methods can be misleading.

SUBMITTER: Massingham T

PROVIDER: S-EPMC1449526 | biostudies-literature | 2005 Mar

REPOSITORIES: biostudies-literature

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Detecting amino acid sites under positive selection and purifying selection.

Massingham Tim T Goldman Nick N

Genetics 20050116 3

An excess of nonsynonymous over synonymous substitution at individual amino acid sites is an important indicator that positive selection has affected the evolution of a protein between the extant sequences under study and their most recent common ancestor. Several methods exist to detect the presence, and sometimes location, of positively selected sites in alignments of protein-coding sequences. This article describes the "sitewise likelihood-ratio" (SLR) method for detecting nonneutral evolutio ...[more]

PMID: 15654091

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Project description:Diverse life forms are driven by the evolution of gene regulatory programs including changes in regulator proteins and cis-regulatory elements. Alterations of cis-regulatory elements are likely to dominate the evolution of the gene regulatory networks, as they are subjected to smaller selective constraints compared with proteins and hence may evolve quickly to adapt the environment. Prior studies on cis-regulatory element evolution focus primarily on sequence substitutions of known transcription factor-binding motifs. However, evolutionary models for the dynamics of motif occurrence are relatively rare, and comprehensive characterization of the evolution of all possible motif sequences has not been pursued. In the present study, we propose an algorithm to estimate the strength of purifying selection of a motif sequence based on an evolutionary model capturing the birth and death of motif occurrences on promoters. We term this measure as the 'evolutionary retention coefficient', as it is related yet distinct from the canonical definition of selection coefficient in population genetics. Using this algorithm, we estimate and report the evolutionary retention coefficients of all possible 10-nucleotide sequences from the aligned promoter sequences of 27 748. orthologous gene families in 34 mammalian species. Intriguingly, the evolutionary retention coefficients of motifs are intimately associated with their functional relevance. Top-ranking motifs (sorted by evolutionary retention coefficients) are significantly enriched with transcription factor-binding sequences according to the curated knowledge from the TRANSFAC database and the ChIP-seq data generated from the ENCODE Consortium. Moreover, genes harbouring high-scoring motifs on their promoters retain significantly coherent expression profiles, and those genes are over-represented in the functional classes involved in gene regulation. The validation results reveal the dependencies between natural selection and functions of cis-regulatory elements and shed light on the evolution of gene regulatory networks.

Dataset Information

Detecting amino acid sites under positive selection and purifying selection.

Publications

Detecting amino acid sites under positive selection and purifying selection.

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