A heuristic Bayesian method for segmenting DNA sequence alignments and detecting evidence for recombination and gene...

Area of Science:

• Computational Biology
• Bioinformatics
• Evolutionary Genetics

Background:

• Detecting recombination and gene conversion is crucial for understanding DNA sequence evolution.
• Phylogenetic methods are widely used but can be sensitive to noise and limited data in short sequences.

Purpose of the Study:

• To propose a robust heuristic approach for identifying evidence of recombination and gene conversion in DNA sequence alignments.
• To improve the statistical significance of detecting these evolutionary events.

Main Methods:

• A two-stage approach involving a sliding window analysis with Markov Chain Monte Carlo (MCMC) sampling of phylogenetic trees.
• Clustering of sampled trees using Robinson-Foulds distance to obtain posterior distributions over tree clusters.
• Bayesian hidden Markov model (HMM) post-processing to identify significant changes in tree distributions, indicating recombination.
• Reversible jump MCMC for determining the number of hidden states, corresponding to recombinant regions.

Main Results:

• The method generates posterior distributions over phylogenetic tree topology clusters for each window position.
• The Bayesian HMM effectively identifies significant changes in these distributions, signaling potential recombination or gene conversion.
• The number of recombinant regions can be inferred by sampling the number of hidden states in the HMM.

Conclusions:

• The proposed heuristic method offers a statistically robust framework for detecting recombination and gene conversion.
• The integration of phylogenetic clustering and Bayesian HMM provides a powerful tool for analyzing DNA sequence evolution.
• This approach enhances the ability to identify and characterize recombinant segments within DNA alignments.