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SlidingBayes: exploring recombination using a sliding window approach based on Bayesian phylogenetic inference.

D Paraskevis¹, K Deforche, P Lemey

¹Laboratory for Clinical and Epidemiological Virology, Rega Institute for Medical Research, Katholieke Universiteit Leuven, Minderbroedersstraat 10, B-3000 Leuven, Belgium. dparask@cc.uoa.gr

Bioinformatics (Oxford, England)

|November 18, 2004

Summary

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We created SlidingBayes, a software tool for recombination analysis using Bayesian phylogenetic inference. This method enhances detection of recombination in complex viral sequences like HIV-1, outperforming simpler techniques.

Area of Science:

Computational Biology
Phylogenetics
Bioinformatics

Background:

Recombination analysis is crucial for understanding viral evolution, particularly for complex genomes like HIV-1.
Existing methods like neighbor joining (NJ) may lack power for highly divergent sequences.

Purpose of the Study:

To develop a novel software tool, SlidingBayes, for robust recombination detection.
To provide a more powerful approach for analyzing recombination in challenging datasets.

Main Methods:

SlidingBayes utilizes Bayesian phylogenetic inference and guides Markov Chain Monte Carlo (MCMC) sampling.
The tool employs a sliding window approach (Bayesian scanning) across sequence alignments.
It supports both nucleotide and amino acid sequence analyses.

Related Experiment Videos

Main Results:

SlidingBayes offers enhanced power for detecting recombination, especially in highly divergent sequences.
The software effectively analyzes complex HIV-1 recombinants where simpler methods may fail.
It integrates MrBayes' modeling capabilities with visualization of phylogenetic clustering support.

Conclusions:

SlidingBayes represents a significant advancement in computational tools for recombination analysis.
The tool provides a reliable method for exploring complex evolutionary relationships and identifying recombinant sequences.