Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/59091

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dc.contributor.authorBlanco-Míguez, Aitorpor
dc.contributor.authorBlanco, Guillermopor
dc.contributor.authorGutierrez-Jácome, Albertopor
dc.contributor.authorFdez-Riverola, Florentinopor
dc.contributor.authorSánchez, Borjapor
dc.contributor.authorLourenço, Análiapor
dc.date.accessioned2019-02-18T11:03:50Z-
dc.date.available2019-02-18T11:03:50Z-
dc.date.issued2019-
dc.identifier.citationBlanco-Míguez, Aitor; Blanco, Guillermo; Gutierrez-Jácome, Alberto; Fdez-Riverola, Florentino; Sánchez, Borja; Lourenço, Anália, Computational prediction of the bioactivity potential of proteomes based on expert knowledge. Journal of Biomedical Informatics, 91(103121), 2019por
dc.identifier.issn1532-0464por
dc.identifier.urihttps://hdl.handle.net/1822/59091-
dc.description.abstractAdvances in the field of genome sequencing have enabled a comprehensive analysis and annotation of the dynamics of the protein inventory of cells. This has been proven particularly rewarding for microbial cells, for which the majority of proteins are already accessible to analysis through automatic metagenome annotation. The large-scale in silico screening of proteomes and metaproteomes is key to uncover bioactivities of translational, clinical and biotechnological interest, and to help assign functions to certain proteins, such as those predicted as hypothetical. This work introduces a new method for the prediction of the bioactivity potential of proteomes/metaproteomes, supporting the discovery of functionally relevant proteins based on prior knowledge. This methodology complements functional annotation enrichment methods by allowing the assignment of functions to proteins annotated as hypothetical/putative/uncharacterised, as well as and enabling the detection of specific bioactivities and the recovery of proteins from defined taxa. This work shows how the new method can be applied to screen proteome and metaproteome sets to obtain predictions of clinical or biotechnological interest based on reference datasets. Notably, with this methodology, the large information files obtained after DNA sequencing or protein identification experiments can be associated for translational purposes that, in cases such as antibiotic-resistance pathogens or foodborne diseases, may represent changes in how these important and global health burdens are approached in the clinical practice. Finally, the Sequence-based Expert-driven pRoteome bioactivity Prediction EnvironmENT, a public Web service implemented in Scala functional programming style, is introduced as means to ensure broad access to the method as well as to discuss main implementation issues, such as modularity, extensibility and interoperability.por
dc.description.sponsorshipThis work was supported by the Spanish “Programa Estatal de Investigación, Desarrollo e Inovación Orientada a los Retos de la Sociedad” (grant AGL2013-44039R); the Asociación Española Contra el Cancer (“Obtención de péptidos bioactivos contra el Cáncer Colo-Rectal a partir de secuencias genéticas de microbiomas intestinales”, grant PS2016). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145- FEDER006684). SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure.por
dc.language.isoengpor
dc.publisherElsevier 1por
dc.rightsopenAccesspor
dc.subjectProteomespor
dc.subjectMetaproteomespor
dc.subjectFunctionally relevant proteinspor
dc.subjectBioactivity predictionpor
dc.subjectTranslational applicationpor
dc.titleComputational prediction of the bioactivity potential of proteomes based on expert knowledgepor
dc.typearticle-
dc.peerreviewedyespor
dc.relation.publisherversionhttp://www.journals.elsevier.com/journal-of-biomedical-informatics/por
dc.commentsCEB50530por
oaire.citationIssue103121por
oaire.citationConferencePlaceUnited States-
oaire.citationVolume91por
dc.date.updated2019-02-17T12:48:43Z-
dc.identifier.eissn1532-0464por
dc.identifier.doi10.1016/j.jbi.2019.103121por
dc.identifier.pmid30738947por
dc.description.publicationversioninfo:eu-repo/semantics/publishedVersionpor
dc.subject.wosScience & Technologypor
sdum.journalJournal of Biomedical Informaticspor
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