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Nucleic Acids Res. 2017 Jan 4;45(D1):D339-D346. doi: 10.1093/nar/gkw1075. Epub 2016 Nov 28.

Protein Ontology (PRO): enhancing and scaling up the representation of protein entities.

Author information

1
Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA dan5@georgetown.edu.
2
Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA.
3
The Jackson Laboratory, Bar Harbor, ME 04609, USA.
4
Oral Diagnostic Sciences, University at Buffalo School of Dental Medicine, Buffalo, NY 14214, USA.
5
Department of Biochemistry & Molecular Pharmacology, NYU School of Medicine, New York, NY 10016, USA.
6
Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA.
7
New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, Buffalo, NY 14203, USA.
8
Roswell Park Cancer Institute, Buffalo, NY 14203, USA.
9
Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA.
10
National Center for Ontological Research, University at Buffalo, Buffalo, NY 14214, USA.

Abstract

The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translational modification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities.

PMID:
27899649
PMCID:
PMC5210558
DOI:
10.1093/nar/gkw1075
[Indexed for MEDLINE]
Free PMC Article

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