The mission of the Mouse Genome Database (MGD) is to facilitate the use of mouse as a model system for understanding human biology and disease. To meet this objective in an era of rapidly emerging genome scale data sets for mouse and human, we serve three primary user communities: researchers who use mouse to investigate the genetic and molecular underpinnings of biology and disease processes, clinical researchers who use mouse models - to study. Specific human diseases and the computational biology/informatics community who leverage MGD's data integration capacity to develop algorithms and data analysis tools. To support the mouse genetics and genomics community we will (1) maintain the canonical, comprehensive catalog of mouse genes and other genome features, (2) annotate mouse genome features with their functional attributes using the Gene Ontology (GO), and (3) annotate mutant genotypes with Mammalian Phenotype (MP) Ontology terms, and curate those that are experimentally determined models of human genetic disease. Cross cutting all of these areas are the enforcement of official nomenclature for genome features, alleles, and mouse strains. To enhance MGD as a tool for the clinical research community we will expand our current coverage of Mendelian inherited human genetic disease models to include non- Mendelian diseases, such as metabolic syndromes, as well as susceptibility and modifier effects defined by QTL. To accomplish this, we will utilize current disease vocabularies and substantially contribute to emerging ontologies for human diseases and conditions. We will develop human-genome as well as mouse-genome views of disease loci and candidate genes. To enhance our support for the computational biology/informatics community we will further develop our Web Services API, Mouse BioMart, and Batch Query Tool for computational users. To meet the aims of this proposal we will maintain cost-effective hardware and software using industry best practices. To ensure the greatest impact of MGD in the broader scientific community we have a dedicated user support staff to provide technical assistance and training for our database users;we actively solicit community input, data submissions, and collaborations. We will continue to make all data in MGD freely available to all.
Virtually all advances in human medicine rely on the use of animal models. Chief among these is the laboratory mouse. In this era of genome-scale, data-driven biomedical research, the Mouse Genome Database (MGD) plays a pivotal role in standardizing, integrating, and disseminating information about the laboratory mouse. MGD's goal is to facilitate use of the mouse as a model system for understanding human biology and disease and to enable development of new hypotheses for and new discoveries in human medicine.
| Roncaglia, Paola; van Dam, Teunis J P; Christie, Karen R et al. (2017) The Gene Ontology of eukaryotic cilia and flagella. Cilia 6:10 |
| Knowlton, Michelle N; Smith, Cynthia L (2017) Naming CRISPR alleles: endonuclease-mediated mutation nomenclature across species. Mamm Genome 28:367-376 |
| Eppig, Janan T (2017) Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse. ILAR J 58:17-41 |
| Eppig, Janan T; Smith, Cynthia L; Blake, Judith A et al. (2017) Mouse Genome Informatics (MGI): Resources for Mining Mouse Genetic, Genomic, and Biological Data in Support of Primary and Translational Research. Methods Mol Biol 1488:47-73 |
| Natale, Darren A; Arighi, Cecilia N; Blake, Judith A et al. (2017) Protein Ontology (PRO): enhancing and scaling up the representation of protein entities. Nucleic Acids Res 45:D339-D346 |
| Köhler, Sebastian; Vasilevsky, Nicole A; Engelstad, Mark et al. (2017) The Human Phenotype Ontology in 2017. Nucleic Acids Res 45:D865-D876 |
| Blake, Judith A; Eppig, Janan T; Kadin, James A et al. (2017) Mouse Genome Database (MGD)-2017: community knowledge resource for the laboratory mouse. Nucleic Acids Res 45:D723-D729 |
| Loughner, Chelsea L; Bruford, Elspeth A; McAndrews, Monica S et al. (2016) Organization, evolution and functions of the human and mouse Ly6/uPAR family genes. Hum Genomics 10:10 |
| Hill, David P; D'Eustachio, Peter; Berardini, Tanya Z et al. (2016) Modeling biochemical pathways in the gene ontology. Database (Oxford) 2016: |
| Lek, Monkol; Karczewski, Konrad J; Minikel, Eric V et al. (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536:285-91 |
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