Mouse Phenome Database: an integrative database and analysis suite for curated empirical phenotype data from laboratory mice. Issue Volume 46:Issue D1(2018) (10th November 2017)

• Record Type: Journal Article
• Authors: Bogue, Molly A; Grubb, Stephen C; Walton, David O; Philip, Vivek M; Kolishovski, Georgi; Stearns, Tim; Dunn, Matthew H; Skelly, Daniel A; Kadakkuzha, Beena; TeHennepe, Gregg; Kunde-Ramamoorthy, Govindarajan; Chesler, Elissa J
• Is Part Of: Nucleic acids research. Volume 46:Issue D1(2018)
• Page Start: D843
• Page End: D850
• Publication Date: 2017-11-10
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkx1082
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: The Mouse Phenome Database (MPD; https://phenome.jax.org ) is a widely used resource that provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD houses individual animal data with detailed, searchable protocols, and makes these data available to other resources via API. MPD provides rigorous curation of experimental data and supporting documentation using relevant ontologies and controlled vocabularies. Most data in MPD are from inbreds and other reproducible strains such that the data are cumulative over time and across laboratories. The resource has been expanded to include the QTL Archive and other primary phenotype data from mapping crosses as well as advanced high-diversity mouse populations including the Collaborative Cross and Diversity Outbred mice. Furthermore, MPD provides a means of assessing replicability and reproducibility across experimental conditions and protocols, benchmarking assays in users' own laboratories, identifying sensitized backgrounds for making new mouse models with genome editing technologies, analyzing trait co-inheritance, finding the common genetic basis for multiple traits and assessing sex differences and sex-by-genotype interactions.