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A mixture model and a hidden markov model to simultaneously detect recombination breakpoints and reconstruct phylogenies.

Boussau B, Guéguen L, Gouy M - Evol. Bioinform. Online (2009)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Université de Lyon, université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, 43 boulevard du 11 novembre 1918, Villeurbanne F-69622, France. boussau@biomserv.univ-lyon1.fr

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Client-Server architecture to efficiently find a set of topologies that best describe the alignment.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2747125&req=5

f2-ebo-2009-067: Client-Server architecture to efficiently find a set of topologies that best describe the alignment.

Mentions: A parallel algorithm based on a client-server architecture, as described in Figure 2, allows to acknowledge the dependencies between topologies.


A mixture model and a hidden markov model to simultaneously detect recombination breakpoints and reconstruct phylogenies.

Boussau B, Guéguen L, Gouy M - Evol. Bioinform. Online (2009)

Client-Server architecture to efficiently find a set of topologies that best describe the alignment.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2747125&req=5

f2-ebo-2009-067: Client-Server architecture to efficiently find a set of topologies that best describe the alignment.
Mentions: A parallel algorithm based on a client-server architecture, as described in Figure 2, allows to acknowledge the dependencies between topologies.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Université de Lyon, université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, 43 boulevard du 11 novembre 1918, Villeurbanne F-69622, France. boussau@biomserv.univ-lyon1.fr

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus