The goal of the Rat Genome Database (RGD) is to provide a research platform that delivers the data and tools necessary for investigators to advance preclinical and translational research. This involves acquiring, validating and integrating comprehensive genetic, genomic, phenotype and disease datasets for rat as well as human, mouse and other mammals used as disease models. One hallmark of the RGD resource has been the development of innovative data mining, presentation and analysis tools. RGD's user community has long included those using rat as a disease model, those doing cross-organism studies particularly with mouse and human, clinical researchers looking for models to understand the impact of genetic and environmental variations on disease mechanisms and those using informatics and computational approaches to analyze data. Increasingly, users are looking for access to data on precision models to validate and replicate potential genetic and environmental factors identified through clinical sequencing projects that may impact disease onset, progression or treatment. To support a diverse user community, we will 1) continue to acquire, validate, analyze, and integrate genomic data with increasing emphasis on variants, non-coding and regulatory elements for rat, human, mouse and multiple other mammals, 2) expand functional annotations beyond disease, phenotype, Gene Ontology, pathway and drug/chemical-gene interactions to expression, metabolome and microbiome data along with the analyses that provide connections among these and insight into commonalities among elements of datasets, and 3) provide quantitative phenotype profiles and expected ranges for individual strain models and controls along with genotype profiles with expansions to other mammals to assist researchers in identifying appropriate models for their studies. RGD will integrate data from multiple organisms into its tools and expand its genomic tools including the development of a comparative map tool. We will continue to assign official nomenclature to all rat genomic elements, mapped phenotypes and strains as part of our quality control efforts, resolve conflicts and identification for rat data integrated from multiple sources and return corrected data to originating sources. RGD will expand REST APIs and FTP site files to accommodate new and analyzed datasets and will continue to support users of multiple browsers and devices.

Public Health Relevance

The rat has been a primary animal model used to study many diseases and physiological processes because of the rich genomic, genetic and phenotype data available. These datasets, along with those for human and other mammal models, are critical to advancing our knowledge of disease processes to develop new diagnostic, preventative and treatment approaches. The primary goal of the Rat Genome Database is to standardize and validate data, provide additional functional information from the vast published literature and provide software tools for researchers to mine and analyze data to further their research into human health and disease.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL064541-22
Application #
10071969
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Luo, James
Project Start
1999-09-30
Project End
2022-12-31
Budget Start
2021-01-01
Budget End
2021-12-31
Support Year
22
Fiscal Year
2021
Total Cost
Indirect Cost
Name
Medical College of Wisconsin
Department
Engineering (All Types)
Type
Schools of Medicine
DUNS #
937639060
City
Milwaukee
State
WI
Country
United States
Zip Code
53226
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Petri, Victoria; Hayman, G Thomas; Tutaj, Marek et al. (2016) Disease, Models, Variants and Altered Pathways-Journeying RGD Through the Magnifying Glass. Comput Struct Biotechnol J 14:35-48
Wang, Shur-Jen; Laulederkind, Stanley J F; Hayman, G Thomas et al. (2016) Comprehensive coverage of cardiovascular disease data in the disease portals at the Rat Genome Database. Physiol Genomics 48:589-600
Huntley, Rachael P; Sitnikov, Dmitry; Orlic-Milacic, Marija et al. (2016) Guidelines for the functional annotation of microRNAs using the Gene Ontology. RNA 22:667-76
Shimoyama, Mary; Laulederkind, Stanley J F; De Pons, Jeff et al. (2016) Exploring human disease using the Rat Genome Database. Dis Model Mech 9:1089-1095
Hayman, G Thomas; Laulederkind, Stanley J F; Smith, Jennifer R et al. (2016) The Disease Portals, disease-gene annotation and the RGD disease ontology at the Rat Genome Database. Database (Oxford) 2016:
Prokop, Jeremy W; Petri, Victoria; Shimoyama, Mary E et al. (2015) Structural libraries of protein models for multiple species to understand evolution of the renin-angiotensin system. Gen Comp Endocrinol 215:106-16
Liu, Weisong; Laulederkind, Stanley J F; Hayman, G Thomas et al. (2015) OntoMate: a text-mining tool aiding curation at the Rat Genome Database. Database (Oxford) 2015:
Shimoyama, Mary; De Pons, Jeff; Hayman, G Thomas et al. (2015) The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease. Nucleic Acids Res 43:D743-50
Wang, Shur-Jen; Laulederkind, Stanley J F; Hayman, G Thomas et al. (2015) PhenoMiner: a quantitative phenotype database for the laboratory rat, Rattus norvegicus. Application in hypertension and renal disease. Database (Oxford) 2015:

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