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

Christophe Georgescu, Abigail L. Manson McGuire, Alexander D. Griggs, Christopher A. Desjardins, Alejandro Pironti, Ilan Wapinski, Thomas Abeel, Brian J. Haas, Ashlee M. Earl

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
52 Downloads (Pure)

Abstract

Accurate orthologue identification is a vital component of bacterial comparative genomic studies, but many popular sequence-similarity-based approaches do not scale well to the large numbers of genomes that are now generated routinely. Furthermore, most approaches do not take gene synteny into account, which is useful information for disentangling paralogues. Here, we present SynerClust, a user-friendly synteny-aware tool based on synergy that can process thousands of genomes. SynerClust was designed to analyse genomes with high levels of local synteny, particularly prokaryotes, which have operon structure. SynerClust’s run-time is optimized by selecting cluster representatives at each node in the phylogeny; thus, avoiding the need for exhaustive pairwise similarity searches. In benchmarking against Roary, Hieranoid2, PanX and Reciprocal Best Hit, SynerClust was able to more completely identify sets of core genes for datasets that included diverse strains, while using substantially less memory, and with scalability comparable to the fastest tools. Due to its scalability, ease of installation and use, and suitability for a variety of computing environments, orthogroup clustering using SynerClust will enable many large-scale prokaryotic comparative genomics efforts.
Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalMicrobial Genomics
Volume4
Issue number11
DOIs
Publication statusPublished - 2018

Keywords

  • orthologues
  • orthogroup clustering
  • synteny
  • comparative genomics

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