ArticleProceedings of the National Academy of Sciences of the United States of America2006
A Dirichlet process model for detecting positive selection in protein-coding DNA sequences.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
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- Expectation-Maximization enables Phylogenetic Dating under a Categorical Rate Model.Systematic biology · 2024Article
- Impact of transient acquired hypermutability on the inter- and intra-species competitiveness of Pseudomonas aeruginosa.The ISME journal · 2023Article
- Molecular characterization and evolution of the resident population of some alfalfa mosaic virus (AMV) isolates in Egypt.BMC microbiology · 2023Article
- Genes and sites under adaptation at the phylogenetic scale also exhibit adaptation at the population-genetic scale.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A Bayesian Mutation-Selection Framework for Detecting Site-Specific Adaptive Evolution in Protein-Coding Genes.Molecular biology and evolution · 2021Article
- Extent and Dynamics of Polymorphism in the Malaria Vaccine CandidateThe American journal of tropical medicine and hygiene · 2018Article
- Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models.Biology direct · 2017Article
- Detecting Adaptation in Protein-Coding Genes Using a Bayesian Site-Heterogeneous Mutation-Selection Codon Substitution Model.Molecular biology and evolution · 2017Article
- Probabilistic models of eukaryotic evolution: time for integration.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2015Review
- The relationship between dN/dS and scaled selection coefficients.Molecular biology and evolution · 2015Article
- Differential selection in HIV-1 gp120 between subtype B and East Asian variant B'.Virologica Sinica · 2014Article
- A Dirichlet process model for classifying and forecasting epidemic curves.BMC infectious diseases · 2014Article
- A phylogenetic model for the detection of epistatic interactions.Molecular biology and evolution · 2013Article
- FUBAR: a fast, unconstrained bayesian approximation for inferring selection.Molecular biology and evolution · 2013Article
- Bayesian selection of nucleotide substitution models and their site assignments.Molecular biology and evolution · 2013Article
- The dynamics of alternative pathways to compensatory substitution.BMC bioinformatics · 2013Article
- Integrating sequence variation and protein structure to identify sites under selection.Molecular biology and evolution · 2013Article
- A hierarchical Bayesian model for calibrating estimates of species divergence times.Systematic biology · 2012Article
- A dirichlet process prior for estimating lineage-specific substitution rates.Molecular biology and evolution · 2012Article
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4 authors.
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Abstract
Most methods for detecting Darwinian natural selection at the molecular level rely on estimating the rates or numbers of nonsynonymous and synonymous changes in an alignment of protein-coding DNA sequences. In some of these methods, the nonsynonymous rate of substitution is allowed to vary across the sequence, permitting the identification of single amino acid positions that are under positive natural selection. However, it is unclear which probability distribution should be used to describe how the nonsynonymous rate of substitution varies across the sequence. One widely used solution is to model variation in the nonsynonymous rate across the sequence as a mixture of several discrete or continuous probability distributions. Unfortunately, there is little population genetics theory to inform us of the appropriate probability distribution for among-site variation in the nonsynonymous rate of substitution. Here, we describe an approach to modeling variation in the nonsynonymous rate of substitution by using a Dirichlet process mixture model. The Dirichlet process allows there to be a countably infinite number of nonsynonymous rate classes and is very flexible in accommodating different potential distributions for the nonsynonymous rate of substitution. We implemented the model in a fully Bayesian approach, with all parameters of the model considered as random variables.
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