Binary Particle Swarm Optimization versus Hybrid Genetic Algorithm for Inferring Well Supported Phylogenetic Trees

TL;DR

This paper compares binary particle swarm optimization and hybrid genetic algorithms for selecting the largest subset of core genes to infer well-supported phylogenetic trees from chloroplast genomes, addressing computational challenges.

Contribution

It introduces a distributed BPSO method for gene subset selection and compares its effectiveness with a hybrid genetic algorithm approach.

Findings

Distributed BPSO effectively identifies well-supported gene subsets.

Hybrid approach yields comparable or improved phylogenetic support.

Results are promising for different plant families.

Abstract

The amount of completely sequenced chloroplast genomes increases rapidly every day, leading to the possibility to build large-scale phylogenetic trees of plant species. Considering a subset of close plant species defined according to their chloroplasts, the phylogenetic tree that can be inferred by their core genes is not necessarily well supported, due to the possible occurrence of problematic genes (i.e., homoplasy, incomplete lineage sorting, horizontal gene transfers, etc.) which may blur the phylogenetic signal. However, a trustworthy phylogenetic tree can still be obtained provided such a number of blurring genes is reduced. The problem is thus to determine the largest subset of core genes that produces the best-supported tree. To discard problematic genes and due to the overwhelming number of possible combinations, this article focuses on how to extract the largest subset of sequences in order to obtain the most supported species tree. Due to computational complexity, a distributed Binary Particle Swarm Optimization (BPSO) is proposed in sequential and distributed fashions. Obtained results from both versions of the BPSO are compared with those computed using an hybrid approach embedding both genetic algorithms and statistical tests. The proposal has been applied to different cases of plant families, leading to encouraging results for these families.