Abstract

Optimal curation of human diseases requires an ontology or structured vocabulary that contains terms familiar to end users, is robust enough to support multiple levels of annotation granularity, is limited to disease terms and is stable enough to avoid extensive reannotation following updates. At Mouse Genome Informatics (MGI), we currently use disease terms from Online Mendelian Inheritance in Man (OMIM) to curate mouse models of human disease. While OMIM provides highly detailed disease records that are familiar to many in the medical community, it lacks structure to support multilevel annotation. To improve disease annotation at MGI, we evaluated the merged Medical Subject Headings (MeSH) and OMIM disease vocabulary created by the Comparative Toxicogenomics Database (CTD) project. Overlaying MeSH onto OMIM provides hierarchical access to broad disease terms, a feature missing from the OMIM. We created an extended version of the vocabulary to meet the genetic disease-specific curation needs at MGI. Here we describe our evaluation of the CTD application, the extensions made by MGI and discuss the strengths and weaknesses of this approach.Database URL: http://www.informatics.jax.org/

Highlights

  • The ability to curate disease-related data is imperative for many databases

  • MErged DIsease voCabulary (MEDIC) was evaluated to determine its suitability for use in curation of mouse models of human disease by Mouse Genome Informatics (MGI)

  • To determine the breadth of coverage, the full set of Online Mendelian Inheritance in Man (OMIM) diseases in use at MGI was compared with the 4049 OMIM terms included in MEDIC at the time of analysis

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Summary

Introduction

The ability to curate disease-related data is imperative for many databases. There is a growing pool of available disease-related data, increasing interest from end users and pressure from funding agencies to make clear connections between the data from model organisms and the human diseases they reference. There are a number of disease vocabularies and ontologies that may be used for the purpose of annotating disease models each with its own advantages and disadvantages [1]

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