Stochastic models for horizontal gene transfer: taking a random walk through tree space.

Area of Science:

• Evolutionary biology
• Genetics
• Bioinformatics

Background:

• Horizontal gene transfer (HGT) is a significant evolutionary mechanism with broad biological and medical relevance.
• Understanding HGT dynamics is crucial for reconstructing evolutionary histories and comprehending genome evolution.

Purpose of the Study:

• To develop and apply novel stochastic models for analyzing HGT using multiple orthologous gene alignments.
• To investigate the impact of HGT on phylogenetic inference and test evolutionary hypotheses, such as the complexity hypothesis.

Main Methods:

• A hierarchical phylogenetic framework incorporating random walks in "tree space" was employed.
• Two random walk models, subtree prune and regraft (SPR) and complete graph walks, were considered.
• A Markov chain Monte Carlo algorithm within a Bayesian framework was developed for model fitting.

Main Results:

• The SPR model demonstrated superior performance compared to the alternative random walk model.
• Phylogenetic reconstruction using 16S rRNA gene alignments was identified as the most probable species tree.
• Analysis indicated a higher frequency of HGT for operational genes relative to informational genes.

Conclusions:

• The proposed stochastic models provide a flexible framework for studying HGT.
• Findings support increased HGT in operational genes, offering insights into genome evolution and potentially challenging the complexity hypothesis.