Bridging trees for posterior inference on ancestral recombination graphs

Area of Science:

• Computational Biology
• Bioinformatics
• Evolutionary Genetics

Background:

• Inferring the evolutionary history of DNA sequences is crucial for understanding genetic diversity and population dynamics.
• Existing computational methods can be limited by scalability for large genomic datasets.

Purpose of the Study:

• To introduce a novel Markov chain Monte Carlo (MCMC) algorithm for efficient DNA sequence history inference.
• To leverage parallel computing for accelerating phylogenetic and population genetics analyses.

Main Methods:

• Implementation of a new MCMC algorithm within the Arbores software.
• Application of a bridging procedure, enabling independent local computations within the algorithm.
• Facilitation of large-scale parallelization for enhanced computational efficiency.

Main Results:

• The developed algorithm, Arbores, offers a scalable approach to inferring DNA sequence history.
• The bridging procedure allows for significant speedups through parallel processing.
• Demonstrated feasibility for analyzing large-scale DNA sequence datasets.

Conclusions:

• The new MCMC algorithm and Arbores software provide a powerful tool for evolutionary and population genetics research.
• The parallelizable bridging method represents a significant advancement in computational phylogenetics.
• This approach enhances the capacity to analyze complex genetic histories more rapidly.