PhenoDigm: Analyzing Curated Annotations to Associate Animal Models with Human Diseases

Overview

Journal Database (Oxford)

Specialty Biology

Date 2013 May 11

PMID 23660285

Citations 91

Authors

Damian Smedley

Anika Oellrich

Sebastian Kohler

Barbara Ruef

Monte Westerfield

Peter Robinson

Suzanna Lewis

Christopher Mungall

Affiliations

Soon will be listed here.

Abstract

The ultimate goal of studying model organisms is to translate what is learned into useful knowledge about normal human biology and disease to facilitate treatment and early screening for diseases. Recent advances in genomic technologies allow for rapid generation of models with a range of targeted genotypes as well as their characterization by high-throughput phenotyping. As an abundance of phenotype data become available, only systematic analysis will facilitate valid conclusions to be drawn from these data and transferred to human diseases. Owing to the volume of data, automated methods are preferable, allowing for a reliable analysis of the data and providing evidence about possible gene-disease associations. Here, we propose Phenotype comparisons for DIsease Genes and Models (PhenoDigm), as an automated method to provide evidence about gene-disease associations by analysing phenotype information. PhenoDigm integrates data from a variety of model organisms and, at the same time, uses several intermediate scoring methods to identify only strongly data-supported gene candidates for human genetic diseases. We show results of an automated evaluation as well as selected manually assessed examples that support the validity of PhenoDigm. Furthermore, we provide guidance on how to browse the data with PhenoDigm's web interface and illustrate its usefulness in supporting research. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm

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