PhenoDigm: analyzing curated annotations to associate animal models with human diseases

TLDR

Studying model organisms aims to translate findings into human biology and disease, and recent genomic advances generate abundant phenotype data that require systematic, automated analysis to infer gene–disease associations. We propose PhenoDigm, an automated method that compares phenotypes to provide evidence for gene–disease associations. PhenoDigm integrates phenotype data from multiple model organisms and applies intermediate scoring methods to identify strongly supported gene candidates for human diseases. Automated evaluation and selected manual examples demonstrate PhenoDigm’s validity, and its web interface facilitates browsing and supports research. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm.

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