EuroPhenome: a repository for high-throughput mouse phenotyping data

The broad aim of biomedical science in the postgenomic era is to link genomic and phenotype information to allow deeper understanding of the processes leading from genomic changes to altered phenotype and disease. The EuroPhenome project (http://www.EuroPhenome.org) is a comprehensive resource for r...

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Veröffentlicht in:Nucleic acids research 2010-01, Vol.38 (Database issue), p.D577-D585
Hauptverfasser: Morgan, Hugh, Beck, Tim, Blake, Andrew, Gates, Hilary, Adams, Niels, Debouzy, Guillaume, Leblanc, Sophie, Lengger, Christoph, Maier, Holger, Melvin, David, Meziane, Hamid, Richardson, Dave, Wells, Sara, White, Jacqui, Wood, Joe, de Angelis, Martin Hrabé, Brown, Steve D.M, Hancock, John M, Mallon, Ann-Marie
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Sprache:eng
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Zusammenfassung:The broad aim of biomedical science in the postgenomic era is to link genomic and phenotype information to allow deeper understanding of the processes leading from genomic changes to altered phenotype and disease. The EuroPhenome project (http://www.EuroPhenome.org) is a comprehensive resource for raw and annotated high-throughput phenotyping data arising from projects such as EUMODIC. EUMODIC is gathering data from the EMPReSSslim pipeline (http://www.empress.har.mrc.ac.uk/) which is performed on inbred mouse strains and knock-out lines arising from the EUCOMM project. The EuroPhenome interface allows the user to access the data via the phenotype or genotype. It also allows the user to access the data in a variety of ways, including graphical display, statistical analysis and access to the raw data via web services. The raw phenotyping data captured in EuroPhenome is annotated by an annotation pipeline which automatically identifies statistically different mutants from the appropriate baseline and assigns ontology terms for that specific test. Mutant phenotypes can be quickly identified using two EuroPhenome tools: PhenoMap, a graphical representation of statistically relevant phenotypes, and mining for a mutant using ontology terms. To assist with data definition and cross-database comparisons, phenotype data is annotated using combinations of terms from biological ontologies.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkp1007