Gain-loss-duplication models for copy number evolution on a phylogeny: Exact algorithms for computing the likelihood and its gradient

Gene gain-loss-duplication models are commonly based on continuous-time birth–death processes. Employed in a phylogenetic context, such models have been increasingly popular in studies of gene content evolution across multiple genomes. While the applications are becoming more varied and demanding, b...

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Vydané v:Theoretical population biology Ročník 145; s. 80 - 94
Hlavný autor: Csűrös, Miklós
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 01.06.2022
Predmet:
Birth–death-immigration process
Gene content
Genome evolution
Maximum likelihood
Phyletic profile
Phyletic profile
Maximum likelihood
Gene content
Genome evolution
Birth–death-immigration process
ISSN:0040-5809, 1096-0325, 1096-0325
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Shrnutí:Gene gain-loss-duplication models are commonly based on continuous-time birth–death processes. Employed in a phylogenetic context, such models have been increasingly popular in studies of gene content evolution across multiple genomes. While the applications are becoming more varied and demanding, bioinformatics methods for probabilistic inference on copy numbers (or integer-valued evolutionary characters, in general) are scarce. We describe a flexible probabilistic framework for phylogenetic gain-loss-duplication models. The framework is based on a novel elementary representation by dependent random variables with well-characterized conditional distributions: binomial, Pólya (negative binomial), and Poisson. The corresponding graphical model yields exact numerical procedures for computing the likelihood and the posterior distribution of ancestral copy numbers. The resulting algorithms take quadratic time in the total number of copies. In addition, we show how the likelihood gradient can be computed by a linear-time algorithm. •Gene families evolve by gain (lateral transfer), loss, and duplication (GLD).•Birth-death processes model copy number evolution by GLD events along a phylogeny.•We decompose the model into a probabilistic network of ancestral copy numbers.•The transition probabilities follow binomial, pólya or Poisson distributions.•The decomposition leads to clean and fast ancestral inference algorithms.
Bibliografia:ObjectType-Article-1
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ISSN:0040-5809
1096-0325
1096-0325
DOI:10.1016/j.tpb.2022.03.003