ArticleBMC bioinformatics2011
A Bayesian model for gene family evolution.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- De Novo Whole-Genome Sequencing and Assembly of the Yellow-Throated Bunting (Animals : an open access journal from MDPI · 2022Article
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- Olfactory receptor subgenome and expression in a highly olfactory procellariiform seabird.Genetics · 2022Article
- Structure and evolution of the squamate major histocompatibility complex as revealed by twoFrontiers in genetics · 2022Article
- A bird-like genome from a frog: Mechanisms of genome size reduction in the ornate burrowing frog,Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Mirage: estimation of ancestral gene-copy numbers by considering different evolutionary patterns among gene families.Bioinformatics advances · 2021Article
- Model-Based Detection of Whole-Genome Duplications in a Phylogeny.Molecular biology and evolution · 2020Article
- Integrating natural history collections and comparative genomics to study the genetic architecture of convergent evolution.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- A generalized birth and death process for modeling the fates of gene duplication.BMC evolutionary biology · 2015Article
- Amino acid transporter expansions associated with the evolution of obligate endosymbiosis in sap-feeding insects (Hemiptera: sternorrhyncha).BMC evolutionary biology · 2015Article
- DiscML: an R package for estimating evolutionary rates of discrete characters using maximum likelihood.BMC bioinformatics · 2014Article
- A phylogenetic model for understanding the effect of gene duplication on cancer progression.Nucleic acids research · 2014Article
- Robustness of birth-death and gain models for inferring evolutionary events.BMC genomics · 2014Article
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4 authors.
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Abstract
backgroundA birth and death process is frequently used for modeling the size of a gene family that may vary along the branches of a phylogenetic tree. Under the birth and death model, maximum likelihood methods have been developed to estimate the birth and death rate and the sizes of ancient gene families (numbers of gene copies at the internodes of the phylogenetic tree). This paper aims to provide a Bayesian approach for estimating parameters in the birth and death model.
resultsWe develop a Bayesian approach for estimating the birth and death rate and other parameters in the birth and death model. In addition, a Bayesian hypothesis test is developed to identify the gene families that are unlikely under the birth and death process. Simulation results suggest that the Bayesian estimate is more accurate than the maximum likelihood estimate of the birth and death rate. The Bayesian approach was applied to a real dataset of 3517 gene families across genomes of five yeast species. The results indicate that the Bayesian model assuming a constant birth and death rate among branches of the phylogenetic tree cannot adequately explain the observed pattern of the sizes of gene families across species. The yeast dataset was thus analyzed with a Bayesian heterogeneous rate model that allows the birth and death rate to vary among the branches of the tree. The unlikely gene families identified by the Bayesian heterogeneous rate model are different from those given by the maximum likelihood method.
conclusionsCompared to the maximum likelihood method, the Bayesian approach can produce more accurate estimates of the parameters in the birth and death model. In addition, the Bayesian hypothesis test is able to identify unlikely gene families based on Bayesian posterior p-values. As a powerful statistical technique, the Bayesian approach can effectively extract information from gene family data and thereby provide useful information regarding the evolutionary process of gene families across genomes.
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