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
PhenoDigm: analyzing curated annotations to associate animal models with human diseases
D. Smedley,A. Oellrich,S. Köhler,B. Ruef,M. Westerfield,Peter N. Robinson,S. Lewis,C. Mungall
Published 2013 in Database J. Biol. Databases Curation
ABSTRACT
PUBLICATION RECORD
- Publication year
2013
- Venue
Database J. Biol. Databases Curation
- Publication date
2013-05-09
- Fields of study
Biology, Medicine, Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- automated evaluation
A computer-based assessment used to test the performance of the phenotype comparison approach.
Aliases: automatic evaluation
- gene-disease associations
Links between genes and human diseases that the framework aims to support with phenotype evidence.
Aliases: gene-disease links
- human genetic diseases
Inherited human diseases that serve as the target disease set for phenotype-based comparisons.
Aliases: human diseases
- intermediate scoring methods
The scoring layers used to rank and filter phenotype-based candidate gene matches.
Aliases: scoring methods
- manually assessed examples
Selected cases reviewed by hand to check whether the phenotype-based matches are plausible.
Aliases: manual examples
- model organisms
Non-human species whose phenotype data are compared against human disease information.
Aliases: animal models
- phenodigm
An automated framework for comparing phenotype annotations from model organisms and human diseases.
Aliases: Phenotype comparisons for DIsease Genes and Models
- phenotype information
Phenotypic annotation data used as input for comparing model organisms with human disease terms.
Aliases: phenotype data, phenotype annotations
- web interface
The browser-based interface used to explore and browse PhenoDigm data.
Aliases: PhenoDigm web interface
REFERENCES
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