English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/186098
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Computational prediction of the bioactivity potential of proteomes based on expert knowledge

AuthorsBlanco-Míguez, Aitor; Blanco, Guillermo ; Gutiérrez-Jácome, Alberto; Fdez-Riverola, Florentino; Sánchez García, Borja ; Lourenço, Anália
KeywordsProteomes
Metaproteomes
Functionally relevant proteins
Bioactivity prediction
Translational application
Issue DateMar-2019
PublisherElsevier
CitationJournal of Biomedical Informatics 91: 103121 (2019)
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.
Publisher version (URL)http://dx.doi.org/10.1016/j.jbi.2019.103121
URIhttp://hdl.handle.net/10261/186098
DOI10.1016/j.jbi.2019.103121
ISSN1532-0464
E-ISSN1532-0480
Appears in Collections:(IPLA) Artículos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,35 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.