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FunTaxIS-lite: a simple and light solution to investigate protein functions in all living organisms.
Bianca, Federico; Ispano, Emilio; Gazzola, Ermanno; Lavezzo, Enrico; Fontana, Paolo; Toppo, Stefano.
Afiliação
  • Bianca F; Computational Medicine Group (MedComp), Department of Molecular Medicine, University of Padova, Padova, Italy.
  • Ispano E; Computational Medicine Group (MedComp), Department of Molecular Medicine, University of Padova, Padova, Italy.
  • Gazzola E; Computational Medicine Group (MedComp), Department of Molecular Medicine, University of Padova, Padova, Italy.
  • Lavezzo E; Computational Medicine Group (MedComp), Department of Molecular Medicine, University of Padova, Padova, Italy.
  • Fontana P; Research and Innovation Center, Edmund Mach Foundation, San Michele all'Adige, Trento, Italy.
  • Toppo S; Computational Medicine Group (MedComp), Department of Molecular Medicine, University of Padova, Padova, Italy.
Bioinformatics ; 39(9)2023 09 02.
Article em En | MEDLINE | ID: mdl-37672040
ABSTRACT
MOTIVATION Defining the full domain of protein functions belonging to an organism is a complex challenge that is due to the huge heterogeneity of the taxonomy, where single or small groups of species can bear unique functional characteristics. FunTaxIS-lite provides a solution to this challenge by determining taxon-based constraints on Gene Ontology (GO) terms, which specify the functions that an organism can or cannot perform. The tool employs a set of rules to generate and spread the constraints across both the taxon hierarchy and the GO graph.

RESULTS:

The taxon-based constraints produced by FunTaxIS-lite extend those provided by the Gene Ontology Consortium by an average of 300%. The implementation of these rules significantly reduces errors in function predictions made by automatic algorithms and can assist in correcting inconsistent protein annotations in databases. AVAILABILITY AND IMPLEMENTATION FunTaxIS-lite is available on https//www.medcomp.medicina.unipd.it/funtaxis-lite and from https//github.com/MedCompUnipd/FunTaxIS-lite.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Itália

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Itália