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Activity Number: 514 - Advanced Statistical Inference for Stochastic Models of Evolutionary Biology
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #328979 Presentation
Title: Markov-Modulated Continuous-Time Markov Chains to Identify Site- and Branch-Specific Evolutionary Variation
Author(s): Guy Baele*
Companies: KU Leuven
Keywords: phylogenetics; Markov-modulated model; Bayesian inference; MCMC; BEAST; evolution
Abstract:

Sequence evolution along a phylogenetic tree is typically modelled using standard Markov models of character evolution. The Markovian property reflects the common assumption that evolution has no memory and is commonly assumed to be time-homogeneous. However, the evolutionary processes that act at the molecular level are highly variable and different sites are hence faced with different selective constraints, impacting their substitution behaviour. Early observations in the 1970s established that the evolutionary rate of a particular site in coding sequences can be variable across the phylogeny due to the fact that sites critical with respect to the function of a macromolecule may change within the nucleotide sequence over time. We propose to incorporate such time variability through Markov-modulated models (MMMs), which allow the substitution process that governs the evolution of an individual site to change with time. We have implemented MMMs into BEAST, a popular Bayesian phylogenetic inference software package, and use it to show that MMMs can drastically impact the phylogenetic tree estimations process, in examples from bacterial and plastid genome evolution.


Authors who are presenting talks have a * after their name.

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