Dataset Information

A semi-automated methodology for finding lipid-related GO terms.

ABSTRACT: MOTIVATION: Although semantic similarity in Gene Ontology (GO) and other approaches may be used to find similar GO terms, there is yet a method to systematically find a class of GO terms sharing a common property with high accuracy (e.g., involving human curation). RESULTS: We have developed a methodology to address this issue and applied it to identify lipid-related GO terms, owing to the important and varied roles of lipids in many biological processes. Our methodology finds lipid-related GO terms in a semi-automated manner, requiring only moderate manual curation. We first obtain a list of lipid-related gold-standard GO terms by keyword search and manual curation. Then, based on the hypothesis that co-annotated GO terms share similar properties, we develop a machine learning method that expands the list of lipid-related terms from the gold standard. Those terms predicted most likely to be lipid related are examined by a human curator following specific curation rules to confirm the class labels. The structure of GO is also exploited to help reduce the curation effort. The prediction and curation cycle is repeated until no further lipid-related term is found. Our approach has covered a high proportion, if not all, of lipid-related terms with relatively high efficiency. DATABASE URL: http://compbio.ddns.comp.nus.edu.sg/?lipidgo.

SUBMITTER: Fan M

PROVIDER: S-EPMC4160098 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A semi-automated methodology for finding lipid-related GO terms.

Fan Mengyuan M Low Hong Sang HS Wenk Markus R MR Wong Limsoon L

Database : the journal of biological databases and curation 20140910

<h4>Motivation</h4>Although semantic similarity in Gene Ontology (GO) and other approaches may be used to find similar GO terms, there is yet a method to systematically find a class of GO terms sharing a common property with high accuracy (e.g., involving human curation).<h4>Results</h4>We have developed a methodology to address this issue and applied it to identify lipid-related GO terms, owing to the important and varied roles of lipids in many biological processes. Our methodology finds lipid ...[more]

PMID: 25209026

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Project description:BackgroundManual curation of experimental data from the biomedical literature is an expensive and time-consuming endeavor. Nevertheless, most biological knowledge bases still rely heavily on manual curation for data extraction and entry. Text mining software that can semi- or fully automate information retrieval from the literature would thus provide a significant boost to manual curation efforts.ResultsWe employ the Textpresso category-based information retrieval and extraction system (http://www.textpresso.org), developed by WormBase to explore how Textpresso might improve the efficiency with which we manually curate C. elegans proteins to the Gene Ontology's Cellular Component Ontology. Using a training set of sentences that describe results of localization experiments in the published literature, we generated three new curation task-specific categories (Cellular Components, Assay Terms, and Verbs) containing words and phrases associated with reports of experimentally determined subcellular localization. We compared the results of manual curation to that of Textpresso queries that searched the full text of articles for sentences containing terms from each of the three new categories plus the name of a previously uncurated C. elegans protein, and found that Textpresso searches identified curatable papers with recall and precision rates of 79.1% and 61.8%, respectively (F-score of 69.5%), when compared to manual curation. Within those documents, Textpresso identified relevant sentences with recall and precision rates of 30.3% and 80.1% (F-score of 44.0%). From returned sentences, curators were able to make 66.2% of all possible experimentally supported GO Cellular Component annotations with 97.3% precision (F-score of 78.8%). Measuring the relative efficiencies of Textpresso-based versus manual curation we find that Textpresso has the potential to increase curation efficiency by at least 8-fold, and perhaps as much as 15-fold, given differences in individual curatorial speed.ConclusionTextpresso is an effective tool for improving the efficiency of manual, experimentally based curation. Incorporating a Textpresso-based Cellular Component curation pipeline at WormBase has allowed us to transition from strictly manual curation of this data type to a more efficient pipeline of computer-assisted validation. Continued development of curation task-specific Textpresso categories will provide an invaluable resource for genomics databases that rely heavily on manual curation.

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A semi-automated methodology for finding lipid-related GO terms.

Publications

A semi-automated methodology for finding lipid-related GO terms.

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