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A Mixture Model and a Hidden Markov Model to Simultaneously Detect Recombination Breakpoints and Reconstruct Phylogenies

Homologous recombination is a pervasive biological process that affects sequences in all living organisms and viruses. In the presence of recombination, the evolutionary history of an alignment of homologous sequences cannot be properly depicted by a single bifurcating tree: some sites have evolved...

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Detalles Bibliográficos
Autores principales: Boussau, Bastien, Guéguen, Laurent, Gouy, Manolo
Formato: Texto
Lenguaje:English
Publicado: Libertas Academica 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2747125/
https://www.ncbi.nlm.nih.gov/pubmed/19812727
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author Boussau, Bastien
Guéguen, Laurent
Gouy, Manolo
author_facet Boussau, Bastien
Guéguen, Laurent
Gouy, Manolo
author_sort Boussau, Bastien
collection PubMed
description Homologous recombination is a pervasive biological process that affects sequences in all living organisms and viruses. In the presence of recombination, the evolutionary history of an alignment of homologous sequences cannot be properly depicted by a single bifurcating tree: some sites have evolved along a specific phylogenetic tree, others have followed another path. Methods available to analyse recombination in sequences usually involve an analysis of the alignment through sliding-windows, or are particularly demanding in computational resources, and are often limited to nucleotide sequences. In this article, we propose and implement a Mixture Model on trees and a phylogenetic Hidden Markov Model to reveal recombination breakpoints while searching for the various evolutionary histories that are present in an alignment known to have undergone homologous recombination. These models are sufficiently efficient to be applied to dozens of sequences on a single desktop computer, and can handle equivalently nucleotide or protein sequences. We estimate their accuracy on simulated sequences and test them on real data.
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spelling pubmed-27471252009-10-06 A Mixture Model and a Hidden Markov Model to Simultaneously Detect Recombination Breakpoints and Reconstruct Phylogenies Boussau, Bastien Guéguen, Laurent Gouy, Manolo Evol Bioinform Online Methodology Homologous recombination is a pervasive biological process that affects sequences in all living organisms and viruses. In the presence of recombination, the evolutionary history of an alignment of homologous sequences cannot be properly depicted by a single bifurcating tree: some sites have evolved along a specific phylogenetic tree, others have followed another path. Methods available to analyse recombination in sequences usually involve an analysis of the alignment through sliding-windows, or are particularly demanding in computational resources, and are often limited to nucleotide sequences. In this article, we propose and implement a Mixture Model on trees and a phylogenetic Hidden Markov Model to reveal recombination breakpoints while searching for the various evolutionary histories that are present in an alignment known to have undergone homologous recombination. These models are sufficiently efficient to be applied to dozens of sequences on a single desktop computer, and can handle equivalently nucleotide or protein sequences. We estimate their accuracy on simulated sequences and test them on real data. Libertas Academica 2009-06-25 /pmc/articles/PMC2747125/ /pubmed/19812727 Text en © the authors, licensee Libertas Academica Ltd. http://creativecommons.org/licenses/by/2.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/2.0/).
spellingShingle Methodology
Boussau, Bastien
Guéguen, Laurent
Gouy, Manolo
A Mixture Model and a Hidden Markov Model to Simultaneously Detect Recombination Breakpoints and Reconstruct Phylogenies
title A Mixture Model and a Hidden Markov Model to Simultaneously Detect Recombination Breakpoints and Reconstruct Phylogenies
title_full A Mixture Model and a Hidden Markov Model to Simultaneously Detect Recombination Breakpoints and Reconstruct Phylogenies
title_fullStr A Mixture Model and a Hidden Markov Model to Simultaneously Detect Recombination Breakpoints and Reconstruct Phylogenies
title_full_unstemmed A Mixture Model and a Hidden Markov Model to Simultaneously Detect Recombination Breakpoints and Reconstruct Phylogenies
title_short A Mixture Model and a Hidden Markov Model to Simultaneously Detect Recombination Breakpoints and Reconstruct Phylogenies
title_sort mixture model and a hidden markov model to simultaneously detect recombination breakpoints and reconstruct phylogenies
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2747125/
https://www.ncbi.nlm.nih.gov/pubmed/19812727
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