A two-phase approach for detecting recombination in nucleotide sequences

TL;DR

This paper presents a two-phase method combining statistical measures and Bayesian phylogenetics to improve detection of recombination events in nucleotide sequences, addressing variability in method performance.

Contribution

It introduces a novel two-phase approach that enhances recombination detection accuracy and efficiency in large datasets, integrating multiple statistical and Bayesian techniques.

Findings

High-confidence detection of recombination events

Efficient analysis of large datasets

Effective delineation of recombination breakpoints

Abstract

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombinationdetecting approaches can vary, depending on evolutionary events that take place after recombination. We recently evaluated the effects of postrecombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach in delineating breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.