Density-based binning of gene clusters to infer function or evolutionary history using GeneGrouper.

Area of Science:

• Genomics
• Microbial evolution
• Bioinformatics

Background:

• Understanding gene cluster evolution is crucial for inferring evolutionary histories and functions.
• Small genetic differences in conserved gene clusters can lead to significant phenotypic changes.
• The increasing abundance of microbial genomes necessitates tools for analyzing gene content variation.

Purpose of the Study:

• To develop a computational tool for unsupervised grouping of similar gene clusters.
• To provide a population-level understanding of gene content variation and functional homology.
• To identify novel gene variants and their functional impact in microbial populations.

Main Methods:

• Development of GeneGrouper, a command-line tool utilizing a density-based clustering method.
• Benchmarking GeneGrouper for detection of specific gene clusters (e.g., Salmonella enterica LT2 Pdu, Pseudomonas aeruginosa PAO1 Mex) across diverse genomes.
• Application of GeneGrouper to analyze gene cluster diversity in S. enterica genomes and identify novel variants.

Main Results:

• GeneGrouper demonstrated high recall and precision in detecting known gene clusters across 435 genomes.
• Identification of a novel, frequently occurring pduN pseudogene in S. enterica.
• In vivo studies showed that the pduN pseudogene negatively impacted microcompartment formation.
• Demonstrated versatility in clustering distant homologous and variable gene clusters from mobile genetic elements.

Conclusions:

• GeneGrouper is an effective tool for analyzing gene cluster variation in microbial genomics.
• The identified pduN pseudogene highlights the functional impact of gene inactivation in bacterial pathways.
• The software facilitates evolutionary and functional insights into gene clusters across diverse taxa.