Evolutionary inference via the Poisson Indel Process

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Evolutionary inference via the Poisson Indel Process

TitleEvolutionary inference via the Poisson Indel Process
Publication TypeJournal Article
Year of Publication2013
AuthorsBouchard-Cote, A, Jordan, MI
JournalPROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume110
Pagination1160-1166
Date PublishedJAN 22
Type of ArticleArticle
ISSN0027-8424
KeywordsPhylogenetics, point process, sequence homology, systematics
AbstractWe address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.
DOI10.1073/pnas.1220450110