Orthology Clusters from Gene Trees with Possvm.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Identifying orthologous genes is crucial for understanding gene function and evolution.
• Manual annotation of gene families from phylogenetic trees is time-consuming and complex.
• Automated tools are needed to streamline the analysis of large-scale genomic data.

Purpose of the Study:

• To introduce Possvm, a novel computational tool for automated ortholog clustering and gene family classification.
• To leverage phylogenetic tree topology and species overlap for accurate orthology inference.
• To provide phylogeny-aware gene annotations for comparative genomics.

Main Methods:

• Utilizes the species overlap algorithm to infer taxonomic information from gene tree topology.
• Employs the Markov clustering algorithm (MCL) to identify orthologous gene clusters.
• Integrates precomputed phylogenetic trees for analysis.

Main Results:

• Possvm achieves high precision and recall in identifying manually curated orthogroups when using accurate phylogenies.
• The tool automates gene tree inspection and annotation in a highly interpretable way.
• Generated annotations are reusable for downstream comparative genomics studies.

Conclusions:

• Possvm offers an efficient and accurate automated solution for ortholog identification and gene family classification.
• The tool's outputs facilitate deeper insights into gene family evolution.
• Possvm enhances the utility of phylogenetic analyses in genomics research.