EvolClust

EvolClust identifies evolutionarily conserved gene clusters by detecting groups of homologous proteins with conserved gene order across multiple eukaryotic genomes.


Key Features:

  • Pairwise genome comparisons: Predicts conserved gene clusters by identifying groups of homologous proteins that are consistently co-located in at least two genomes.
  • All-versus-all genome comparisons: Infers multi-species cluster families by comparing genomes across a dataset to find related clusters present in multiple species.
  • Conservation significance: Detects clusters that are conserved beyond the level expected for the compared genomes.
  • Broad taxonomic application: Applied to eukaryotic genomes across five major clades: Fungi, Plants, Metazoans, Insects, and Protists.
  • EvolClustDB: Provides pre-computed evolutionarily conserved gene neighborhoods and links gene order information to phylogenetic context to support inference of evolutionary events.

Scientific Applications:

  • Evolutionary genomics: Identification of conserved gene clusters to infer potential regulatory or functional associations among genes.
  • Inference of evolutionary events: Mapping conserved gene order within phylogenetic contexts to infer gene gain, loss, or transfer.
  • Comparative eukaryotic genomics: Discovery and analysis of conserved gene clusters across Fungi, Plants, Metazoans, Insects, and Protists.

Methodology:

Uses pairwise genome comparisons to detect groups of homologous proteins co-located in at least two genomes, employs all-versus-all genome comparisons to infer families of related clusters across species, and pre-computes conserved gene neighborhoods in EvolClustDB for phylogenetic mapping of gene order to infer gain, loss, or transfer.

Topics

Collections

Details

Tool Type:
web application
Programming Languages:
Python
Added:
11/14/2019
Last Updated:
11/24/2024

Operations

Publications

Marcet-Houben M, Gabaldón T. EvolClust: automated inference of evolutionary conserved gene clusters in eukaryotes. Bioinformatics. 2019;36(4):1265-1266. doi:10.1093/bioinformatics/btz706. PMID:31560365. PMCID:PMC7703780.

PMID: 31560365
PMCID: PMC7703780
Funding: - Centro de Excelencia Severo Ochoa: BFU2015-67107, SEV-2012-0208 - Catalan Research Agency: SGR857 - European Union’s Horizon 2020 research and innovation programme: ERC-2016-724173 - Marie Sklodowska-Curie grant: H2020-MSCA-ITN-2014-642095 - INB: ISCIII-SGEFI/ERDF, PT17/0009/0023, –

Marcet-Houben M, Collado-Cala I, Fuentes-Palacios D, Gómez AD, Molina M, Garisoain-Zafra A, Chorostecki U, Gabaldón T. EvolClustDB: Exploring Eukaryotic Gene Clusters with Evolutionarily Conserved Genomic Neighbourhoods. Journal of Molecular Biology. 2023;435(14):168013. doi:10.1016/j.jmb.2023.168013. PMID:36806474.

PMID: 36806474
Funding: - Agència de Gestió d'Ajuts Universitaris i de Recerca: SGR423 - Gordon and Betty Moore Foundation: GBMF9742 - Instituto de Salud Carlos III: CIBERINFEC CB21/13/00061- ISCIII-SGEFI/ERDF, IMP/00019 - “la Caixa” Foundation: LCF/PR/HR21/00737 - Ministerio de Ciencia e Innovación: IJC2019-039402-I, PGC2018-099921-B-I00 - Horizon 2020: ERC-2016-724173 - H2020 Marie Skłodowska-Curie Actions: H2020-MSCA-IF-2017-793699