abstract

• Orthology detection from sequence similarity remains a difficult and computationally expensive problem for gene families with large numbers of gene duplications and losses. REvolutionH-tl implements a new graph-based approach to identify orthogroups, orthology, and paralogy relationships first, and it uses this information in a second step to infer event-labeled gene trees and their reconciliation with an inferred species tree. It avoids using gene trees and species trees upon input and settles for a maximal subtree reconciliation in cases where noise or horizontal gene transfer precludes a global reconciliation. The accuracy of the tool is comparable to competing tools at substantially reduced computational cost. REvolutionH-tl is freely available at https://pypi.org/project/revolutionhtl/. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

publication date

• 2024-01-01

published in

• Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  Journal

keywords

• best matches; evolutionary scenarios; orthology Genes; Graphic methods; Trees (mathematics); Best match; Evolutionary history; Evolutionary scenario; Gene duplications; Gene families; Gene loss; Gene tree; Graph-based; Orthology; Sequence similarity; Gene transfer

Digital Object Identifier (DOI)

• 10.1007/978-3-031-58072-7_5

PubMed ID

start page

• 89

end page

• 109

volume

• 14616 LNBI

issue