Protein Ontology: a controlled structured network of protein entities

Natale, Darren A.; Arighi, Cecilia N.; Blake, Judith A.; Bult, Carol J.; Christie, Karen R.; Cowart, Julie; D'Eustachio, P; Diehl, Alexander D.; Drabkin, Harold J.; Helfer, Olivia; Huang, Hongzhan; Masci, Anna Maria; Ren, Jingyi; Roberts, Natalia V.; Ross, Karen; Ruttenberg, Alan; Shamovsky, Veronica; Smith, Barry; Yerramalla, Meher Shruti; Zhang, Jian; AlJanahi, Aisha A.; Celen, Irem; Gan, Cynthia; Lv, Mengxi; Schuster-Lezell, Emily; Wu, Cathy H.

doi:10.1093/nar/gkt1173

Cited by 63 publications

(64 citation statements)

References 22 publications

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“…The Protein Ontology (PR) [25] represents protein-related entities. This CFP has a large support of 18,207, while having an above the average number of frequent axiom patterns.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Discovery of emerging design patterns in ontologies using tree mining

Ławrynowicz

Potoniec

Robaczyk

et al. 2018

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The research goal of this work is to investigate modeling patterns that recur in ontologies. Such patterns may originate from certain design solutions, and they may possibly indicate emerging ontology design patterns. We describe our tree-mining method for identifying the emerging design patterns. The method works in two steps: (1) we transform the ontology axioms in a tree shape in order to find axiom patterns; and then, (2) we use association analysis to mine co-occuring axiom patterns in order to extract emerging design patterns. We conduct an experimental study on a set of 331 ontologies from the BioPortal repository. We show that recurring axiom patterns appear across all individual ontologies, as well as across the whole set. In individual ontologies, we find frequent and non-trivial patterns with and without variables. Some of the former patterns have more than 300,000 occurrences. The longest pattern without a variable discovered from the whole ontology set has size 12, and it appears in 14 ontologies. To the best of our knowledge, this is the first method for automatic discovery of emerging design patterns in ontologies. Finally, we demonstrate that we are able to automatically detect patterns, for which we have manually confirmed that they are fragments of ontology design patterns described in the literature. Since our method is not specific to particular ontologies, we conclude that we should be able to discover new, emerging design patterns for arbitrary ontology sets.

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“…The Protein Ontology (PR) [25] represents protein-related entities. This CFP has a large support of 18,207, while having an above the average number of frequent axiom patterns.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Discovery of emerging design patterns in ontologies using tree mining

Ławrynowicz

Potoniec

Robaczyk

et al. 2018

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“…Indeed, RLIMS-P 2.0 has been evaluated by curators from PhosphoGrid [49], Phospho.ELM [4] and Protein Ontology (PRO) [2] in BioCreative Interactive Text Mining task [46], [51], and it has been integrated into their curation workflows. The current work focuses on protein phosphorylation information, but the IE pipeline employing the enhanced, generalizable architecture can be readily ported to the extraction of PTM types other than phosphorylation.…”

Section: Discussionmentioning

confidence: 99%

“…In signal transduction networks, it serves as a switch to transmit signals in response to extracellular stimuli and intracellular changes [1]. Protein phosphorylation has been widely studied, and research findings have been curated in several biological knowledgebases, such as Protein Ontology [2], PhosphoSite-Plus [3], Phospho.ELM [4], and UniProt Knowledgebase (UniProtKB) [5]. The manual curation of phosphorylation information reported in literature, however, lags behind because of the ever-increasing publications on this active research topic.…”

Section: Introductionmentioning

confidence: 99%

RLIMS-P 2.0: A Generalizable Rule-Based Information Extraction System for Literature Mining of Protein Phosphorylation Information

Torii

Arighi

et al. 2015

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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We introduce RLIMS-P version 2.0, an enhanced rule-based information extraction (IE) system for mining kinase, substrate, and phosphorylation site information from scientific literature. Consisting of natural language processing and IE modules, the system has integrated several new features, including the capability of processing full-text articles and generalizability towards different post-translational modifications (PTMs). To evaluate the system, sets of abstracts and full-text articles, containing a variety of textual expressions, were annotated. On the abstract corpus, the system achieved F-scores of 0.91, 0.92, and 0.95 for kinases, substrates, and sites, respectively. The corresponding scores on the full-text corpus were 0.88, 0.91, and 0.92. It was additionally evaluated on the corpus of the 2013 BioNLP-ST GE task, and achieved an F-score of 0.87 for the phosphorylation core task, improving upon the results previously reported on the corpus. Full-scale processing of all abstracts in MEDLINE and all articles in PubMed Central Open Access Subset has demonstrated scalability for mining rich information in literature, enabling its adoption for biocuration and for knowledge discovery. The new system is generalizable and it will be adapted to tackle other major PTM types. RLIMS-P 2.0 online system is available online (http://proteininformationresource.org/rlimsp/) and the developed corpora are available from iProLINK (http://proteininformationresource.org/iprolink/).

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“…The Protein Ontology (PRO; see Table 1 for additional abbreviations) provides a flexible way to refer to protein entities at any such level of specificity, as generically or as precisely as needed (2). PRO organizes these entities into classes describing proteins derived from homologs (‘family level’ classes), from a single gene (‘gene level’ classes), from a single transcript (‘sequence level’ classes), or from a set of modifications (‘modification level’ classes).…”

Section: Overviewmentioning

confidence: 99%

“…This has led to increased reliance on computational means to supplement or aid expert curation. For example, PRO imports information from UniProtKB to create terms for selected species (2), and integrates information from the OMA (9) and PANTHER (10) databases to find connections between such terms. However, while such resources are well-suited for gene-level terms, we must turn to other resources to obtain increased information about specific proteoforms containing post-translational modifications.…”

Section: Enhancements Of Data and Coveragementioning

confidence: 99%

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

et al. 2016

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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.

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Protein Ontology: a controlled structured network of protein entities

Cited by 63 publications

References 22 publications

Discovery of emerging design patterns in ontologies using tree mining

Discovery of emerging design patterns in ontologies using tree mining

RLIMS-P 2.0: A Generalizable Rule-Based Information Extraction System for Literature Mining of Protein Phosphorylation Information

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

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