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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
A mixture model and a hidden markov model to simultaneously detect recombination breakpoints and reconstruct
Bastien Boussau1, Laurent Guéguen, Manolo Gouy
1Université 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
Evolutionary Bioinformatics Online
|October 9, 2009
Summary
This study introduces novel computational models to detect homologous recombination breakpoints in biological sequences. These efficient methods accurately identify diverse evolutionary histories within sequence alignments for both DNA and protein data.
Area of Science:
- Computational Biology
- Bioinformatics
- Evolutionary Genetics
Background:
- Homologous recombination is a fundamental biological process impacting evolutionary history reconstruction.
- Traditional methods for analyzing recombination are computationally intensive or limited to specific sequence types.
- Phylogenetic analysis of sequences with recombination requires methods that can account for multiple evolutionary histories.
Purpose of the Study:
- To develop and implement novel computational models for detecting recombination breakpoints.
- To identify and analyze the various evolutionary histories present within homologous sequence alignments.
- To provide efficient tools applicable to both nucleotide and protein sequences.
Main Methods:
- Proposed and implemented a Mixture Model on trees.
- Developed a phylogenetic Hidden Markov Model (HMM).
- Applied these models to identify recombination breakpoints and evolutionary paths in sequence alignments.
Main Results:
- The proposed models efficiently reveal recombination breakpoints.
- These methods successfully identify diverse evolutionary histories within sequence data.
- The models demonstrate accuracy on simulated data and applicability to real biological sequences.
Conclusions:
- The Mixture Model on trees and phylogenetic HMM offer an efficient approach to analyzing homologous recombination.
- These computational tools can be utilized on standard desktop computers for large sequence datasets.
- The models are versatile, handling both nucleotide and protein sequences for comprehensive evolutionary analysis.
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