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- W4287716959 abstract "Phylogenetics uses alignments of molecular sequence data to learn about evolutionary trees. Substitutions in sequences are modelled through a continuous-time Markov process, characterised by an instantaneous rate matrix, which standard models assume is time-reversible and stationary. These assumptions are biologically questionable and induce a likelihood function which is invariant to a tree's root position. This hampers inference because a tree's biological interpretation depends critically on where it is rooted. Relaxing both assumptions, we introduce a model whose likelihood can distinguish between rooted trees. The model is non-stationary, with step changes in the instantaneous rate matrix at each speciation event. Exploiting recent theoretical work, each rate matrix belongs to a non-reversible family of Lie Markov models. These models are closed under matrix multiplication, so our extension offers the conceptually appealing property that a tree and all its sub-trees could have arisen from the same family of non-stationary models. We adopt a Bayesian approach, describe an MCMC algorithm for posterior inference and provide software. The biological insight that our model can provide is illustrated through an analysis in which non-reversible but stationary, and non-stationary but reversible models cannot identify a plausible root." @default.
- W4287716959 created "2022-07-26" @default.
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- W4287716959 date "2020-07-16" @default.
- W4287716959 modified "2023-09-27" @default.
- W4287716959 title "Incorporating compositional heterogeneity into Lie Markov models for phylogenetic inference" @default.
- W4287716959 doi "https://doi.org/10.48550/arxiv.2007.08511" @default.
- W4287716959 hasPublicationYear "2020" @default.
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