From Small Parsimony to Horizontal Gene Transfer: Inferring Horizontal Transfer and Gene Loss for Single-Origin...

Area of Science:

• Evolutionary biology
• Bioinformatics
• Computational biology

Background:

• Character-based models like perfect transfer networks infer horizontal gene transfers.
• Existing models assume rare losses and single origins, limiting their applicability.
• These models can overcomplicate evolutionary scenarios or be too restrictive.

Purpose of the Study:

• To extend existing models by incorporating gene loss events.
• To develop a Sankoff-Rousseau-like algorithm for inferring scenarios with transfers and losses.
• To enable the tracing of gene transfer highways using species tree data.

Main Methods:

• Developed a Sankoff-Rousseau-like algorithm to infer scenarios with gene transfers and losses.
• Linked the small parsimony problem to inferring minimum loss and transfer scenarios.
• Incorporated user-defined penalties for gene losses and transfers.
• Applied the model to a bacterial species dataset with Kyoto Encyclopedia of Genes and Genomes functions.

Main Results:

• The algorithm recovers the simplest scenarios combining gene transfers and losses.
• It effectively uses single character presence-absence data within a species tree.
• The model demonstrates utility in tracing gene transfer pathways.
• A case study on bacterial gene functions validated the model's applicability.

Conclusions:

• The new algorithm provides a more flexible and accurate method for inferring evolutionary histories.
• It effectively models both horizontal gene transfers and gene losses.
• The approach aids in understanding gene flow and evolutionary pathways in bacterial species.