. The Knockout Mouse Phenotyping Project (KOMP) is a critical resource for biomedical research that provides unbiased gene to phenotype associations from genes with little or no-known function, supplying strains for follow-up mechanistic studies and integration across resources to provide new systematic insights into the underlying causes of rare and common disease. The MPI2 Consortium will continue to support KOMP2 and IMPC partners by providing data acquisition, analysis, visualisation, quality control and integration of this valuable dataset. Specifically: ? The Pheno-DCC will develop standardized protocols for new KOMP2 phenotyping tests and continue to support and enhance data upload mechanisms for the KOMP2 production and phenotyping centers. Specialist data wranglers will continue to perform quality control and interact with data submitters through the QC interface platform to address issues and will work with MPI2 developers to extend automated QC tools. Preliminary statistical analysis will be performed after data validation to quickly inform users of potentially interesting strains. ? The statistical analysis and annotation pipelines will be extended to include new tests such as aging studies. The phenotype comparisons to identify candidate disease models will be enhanced by including new disease populations and more extensive semantic mappings between phenotype ontologies. ? The Core Data Archive will continue to store all raw data and its analysis, provide programmatic access, push data to new resources such as NCBI, and integrate KOMP2 data with other EBI resources such as Reactome Pathways and the Expression Atlas ? The MPI2 partners will continue to enhance the single point of access www.mousephenotype.org portal, programmatic access and bulk downloads based on feedback from users including the deployment of online analysis tools
Research into the lab mouse provides a unique opportunity to dissect gene function underlying human rare and common disease and to model disease. This project provides access to millions of data points which will be analysed and integrated with health data from genome wide association studies (GWAS) of common disease, rare disease studies and other model species thus illuminating the function of undescribed genes relevant to human health.
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