SynerClust: a highly scalable, synteny-aware orthologue clustering tool

Area of Science:

• Genomics
• Bioinformatics

Background:

• Accurate orthologue identification is crucial for bacterial comparative genomics.
• Existing methods struggle with scalability and do not leverage gene synteny for paralogue distinction.

Purpose of the Study:

• To introduce SynerClust, a scalable and synteny-aware tool for orthologue identification in large-scale bacterial genomics.
• To improve the accuracy and efficiency of core gene set identification in prokaryotes.

Main Methods:

• SynerClust utilizes gene synteny and a novel clustering approach based on synergy.
• Runtime is optimized by selecting cluster representatives, avoiding exhaustive pairwise similarity searches.
• The tool is designed for prokaryotic genomes, considering their operon structures.

Main Results:

• SynerClust demonstrates superior performance in identifying complete core gene sets for diverse bacterial strains compared to existing tools (Roary, Hieranoid2, PanX, Reciprocal Best Hit).
• The tool uses significantly less memory and offers comparable scalability to the fastest methods.
• SynerClust is user-friendly, easily installable, and suitable for various computing environments.

Conclusions:

• SynerClust provides a scalable and accurate solution for orthogroup clustering in large prokaryotic comparative genomics.
• Its synteny-aware approach enhances orthologue identification, particularly for bacteria.
• The tool facilitates large-scale prokaryotic comparative genomics research.