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OntoMate: a text-mining tool aiding curation at the Rat Genome Database.

Weisong Liu1, Stanley J F Laulederkind2, G Thomas Hayman3

  • 1Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA Human and Molecular Genetics Center, Medical College of Wisconsin, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Department of Physiology, Medical College of Wisconsin and Department of Surgery, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA.

Database : the Journal of Biological Databases and Curation
|January 27, 2015
PubMed
Summary

The Rat Genome Database (RGD) now uses OntoMate, an ontology-driven tool, to enhance gene curation efficiency. This bioNLP-powered system streamlines data extraction from scientific literature, improving RGD

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Area of Science:

  • Bioinformatics
  • Genomics
  • Scientific Literature Curation

Background:

  • Model organism databases, like the Rat Genome Database (RGD), face challenges in converting free-text scientific literature into structured data.
  • Manual curation of gene, Quantitative Trait Locus (QTL), and strain information is labor-intensive and struggles to keep pace with the growing volume of biomedical literature.
  • Advancements in biological natural language processing (bioNLP) offer opportunities to improve curation efficiency.

Purpose of the Study:

  • To improve the efficiency of gene curation at the Rat Genome Database (RGD).
  • To replace the existing PubMed search engine with a more effective tool within the gene curation workflow.
  • To leverage text-mining and ontology-driven approaches for enhanced literature data extraction.

Main Methods:

  • Development and implementation of OntoMate, an ontology-driven, concept-based literature search engine.
  • OntoMate tags abstracts with key entities such as gene names, mutations, and organism names, utilizing RGD's ontologies.
  • Integration of OntoMate into the RGD gene curation workflow, replacing PubMed search and providing linked terms for data entry and browsing.

Main Results:

  • OntoMate successfully tags abstracts with relevant biological terms and entities.
  • The system streamlines the literature search and data import process compared to using PubMed.
  • OntoMate's scalable architecture and specific features accelerate the RGD gene curation process, increasing automation.

Conclusions:

  • The adoption of OntoMate, a bioNLP tool, significantly enhances the efficiency and automation of the RGD gene curation workflow.
  • Ontology-driven text mining provides a more effective method for extracting and structuring data from scientific literature.
  • RGD's initiative demonstrates a successful application of advanced text-mining tools in managing large-scale biological data.