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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Published on: August 20, 2019

Annotating the biomedical literature for the human variome.

Karin Verspoor1, Antonio Jimeno Yepes, Lawrence Cavedon

  • 1National ICT Australia (NICTA), Victoria Research Laboratory, Level 2, Building 193, The University of Melbourne, Parkville VIC 3010, Australia. karin.verspoor@nicta.com.au

Database : the Journal of Biological Databases and Curation
|April 16, 2013
PubMed
Summary

This study presents the Variome Annotation Schema for cataloging human genetic variation and disease links. It demonstrates high agreement in annotating genetic variation data from scientific literature.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Medical Informatics

Background:

  • Cataloging human genetic variation and its link to disease is complex.
  • Existing methods struggle to systematically capture this information from literature.
  • The International Society for Gastrointestinal Hereditary Tumours (InSiGHT) database highlighted a need for a structured approach.

Purpose of the Study:

  • To introduce the Variome Annotation Schema for capturing human genetic variation and disease relationships.
  • To assess the schema's applicability and reliability in annotating scientific literature.
  • To explore the schema's utility for database curation and text mining development.

Main Methods:

  • Developed the Variome Annotation Schema to represent concepts and relations in genetic variation literature.
  • Applied the schema to a corpus of full-text articles on inherited colorectal cancer.
  • Evaluated inter-annotator agreement using F-score for both exact and boundary matching of entities and relations.

Main Results:

  • Achieved high inter-annotator agreement (0.78-0.95 F-score) for entity annotation, improving with relaxed boundary matching (≥0.87 F-score).
  • Demonstrated reliable agreement for several relation types, with 'cohort-has-size' reaching 0.90 F-score.
  • Confirmed the schema's relevance for the InSiGHT database curation process.

Conclusions:

  • The Variome Annotation Schema effectively captures key information on human genetic variation and disease from literature.
  • The schema and annotated corpus are valuable resources for developing text mining tools in human variome research.
  • Text mining holds significant potential for improving the curation of genetic variation data.