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SynerClust

A highly scalable, synteny-aware orthologue clustering tool

Journal Article (2018)
Author(s)

Christophe Georgescu (Broad Institute of MIT and Harvard)

A Manson (Broad Institute of MIT and Harvard)

Alexander D. Griggs (Broad Institute of MIT and Harvard)

Christopher A. Desjardins (Broad Institute of MIT and Harvard)

Alejandro Pironti (Broad Institute of MIT and Harvard)

Ilan Wapinski (enEvolv)

T.E.P.M.F. Abeel (TU Delft - Pattern Recognition and Bioinformatics, Broad Institute of MIT and Harvard)

Brian J. Haas (Broad Institute of MIT and Harvard)

AM Earl (Broad Institute of MIT and Harvard)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2018 C.H. Georgescu, Abigail L. Manson McGuire, Alexander D. Griggs, Christopher A. Desjardins, Alejandro Pironti, Ilan Wapinski, T.E.P.M.F. Abeel, Brian J. Haas, Ashlee M. Earl
DOI related publication
https://doi.org/10.1099/mgen.0.000231
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 C.H. Georgescu, Abigail L. Manson McGuire, Alexander D. Griggs, Christopher A. Desjardins, Alejandro Pironti, Ilan Wapinski, T.E.P.M.F. Abeel, Brian J. Haas, Ashlee M. Earl
Research Group
Pattern Recognition and Bioinformatics
Issue number
11
Volume number
4
Pages (from-to)
1-7
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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.