EvolClust: automated inference of evolutionary conserved gene clusters in eukaryotes

Marina Marcet-Houben; Toni Gabaldón

doi:10.1093/bioinformatics/btz706

EvolClust: automated inference of evolutionary conserved gene clusters in eukaryotes

Bioinformatics. 2020 Feb 15;36(4):1265-1266. doi: 10.1093/bioinformatics/btz706.

Authors

Marina Marcet-Houben^{1

2}, Toni Gabaldón^{1

2

3}

Affiliations

¹ Centre for Genomic Regulation (CRG), Bioinformatics and Genomics department, The Barcelona Institute of Science and Technology, Barcelona 08003, Spain.
² Health and Experimental Sciences Department, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.
³ ICREA, Barcelona 08010, Spain.

Abstract

Motivation: The evolution and role of gene clusters in eukaryotes is poorly understood. Currently, most studies and computational prediction programs limit their focus to specific types of clusters, such as those involved in secondary metabolism.

Results: We present EvolClust, a python-based tool for the inference of evolutionary conserved gene clusters from genome comparisons, independently of the function or gene composition of the cluster. EvolClust predicts conserved gene clusters from pairwise genome comparisons and infers families of related clusters from multiple (all versus all) genome comparisons.

Availability and implementation: https://github.com/Gabaldonlab/EvolClust/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Eukaryota*
Genome
Multigene Family
Software