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

Alexander G. McFarland, Nolan W. Kennedy, Carolyn E. Mills, Danielle Tullman-Ercek, Curtis Huttenhower, Erica M. Hartmann*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Motivation: Identifying variant forms of gene clusters of interest in phylogenetically proximate and distant taxa can help to infer their evolutionary histories and functions. Conserved gene clusters may differ by only a few genes, but these small differences can in turn induce substantial phenotypes, such as by the formation of pseudogenes or insertions interrupting regulation. Particularly as microbial genomes and metagenomic assemblies become increasingly abundant, unsupervised grouping of similar, but not necessarily identical, gene clusters into consistent bins can provide a population-level understanding of their gene content variation and functional homology. Results: We developed GeneGrouper, a command-line tool that uses a density-based clustering method to group gene clusters into bins. GeneGrouper demonstrated high recall and precision in benchmarks for the detection of the 23-gene Salmonella enterica LT2 Pdu gene cluster and four-gene Pseudomonas aeruginosa PAO1 Mex gene cluster among 435 genomes spanning mixed taxa. In a subsequent application investigating the diversity and impact of gene-complete and -incomplete LT2 Pdu gene clusters in 1130 S.enterica genomes, GeneGrouper identified a novel, frequently occurring pduN pseudogene. When investigated in vivo, introduction of the pduN pseudogene negatively impacted microcompartment formation. We next demonstrated the versatility of GeneGrouper by clustering distant homologous gene clusters and variable gene clusters found in integrative and conjugative elements.

Original languageEnglish (US)
Pages (from-to)612-620
Number of pages9
Issue number3
StatePublished - Feb 1 2022

ASJC Scopus subject areas

  • Computational Mathematics
  • Molecular Biology
  • Biochemistry
  • Statistics and Probability
  • Computer Science Applications
  • Computational Theory and Mathematics


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