The Rat Genome Database (RGD, http://rgd.mcw.edu) provides the most comprehensive data repository and informatics platform related to the laboratory rat, one of the most important model organisms for disease studies. RGD maintains and updates datasets for genomic elements such as genes, transcripts and increasingly in recent years, sequence variations, as well as map positions for multiple assemblies and sequence information. Functional annotations for genomic elements are curated from published literature, submitted by researchers and integrated from other public resources. Complementing the genomic data catalogs are those associated with phenotypes and disease, including strains, QTL and experimental phenotype measurements across hundreds of strains. Data are submitted by researchers, acquired through bulk data pipelines or curated from published literature. Innovative software tools provide users with an integrated platform to query, mine, display and analyze valuable genomic and phenomic datasets for discovery and enhancement of their own research. This update highlights recent developments that reflect an increasing focus on: (i) genomic variation, (ii) phenotypes and diseases, (iii) data related to the environment and experimental conditions and (iv) datasets and software tools that allow the user to explore and analyze the interactions among these and their impact on disease.
© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
Variant details available on the Genome Browser. RGD's Rat Genome Browser (GBrowse) contains tracks for a variety of genomic elements, including genes, QTL, congenic strains, markers and strain-specific variants. Informative popups for the latter display information about the type, location and predicted consequences of each variant, as well as information pertaining to the sequence data supporting that variant call.
Search and filter options for variant types in Variant Visualizer. After selecting the genomic assembly and the strains of interest (upper left), and specifying the region, gene or list of genes of interest (upper middle; in this case, a list of genes was entered), the user has the option of filtering the results by the type of variant, its location relative to genes or transcripts, and the call statistics (upper right). If no selections are made on this page, the tool will return all of the variants that meet the input strain and region criteria. Once these selections have been made, the ‘Variant Distribution’ view (lower) shows the number of variants for each gene in which at least one of the strains queried contains at least one variant matching the criteria.
Variant Visualizer results showing location and type of variant within gene structure. Clicking on a gene symbol in the ‘Variant Distribution’ view opens the ‘Variation Overview Plot’ for that gene. Clicking a specific variant opens the variant detail display showing the type, location and predicted consequences of the selected variant, the calculated conservation of that nucleotide across species and information about the sequence data supporting the variant call, such as the read depth.
A) Categories of phenotype measurement records in PhenoMiner. RGD's Clinical Measurement Ontology is a hierarchical vocabulary of specific measurements used both clinically and in the research laboratory. Users can choose a higher level term to see results for all the measurements in that category, or ‘drill down’ to find a specific measurement of interest, such as ‘platelet intracellular calcium level’. ( B) PhenoMiner bar chart results for ‘platelet intracellular calcium level’ comparing values in untreated control rats versus rats treated with drugs such as thapsigargin and thrombin. The display makes it easy to compare across strains, conditions and methods, either within a single study or across multiple studies. In addition, the specific quantitative data can be downloaded for further analysis or comparison with a researcher's own results.
Cisplatin response pathway. RGD's interactive pathway diagrams give a detailed and intricate view of a growing list of regulatory, signaling, metabolic, disease and drug pathways. The page begins with an overview of what is known about that pathway (not shown in this figure), and a diagram showing the various players and their interactions and relationships in the functioning of the pathway. Disease pathways and altered pathways show the details of what can go wrong with a specific interaction to cause a breakdown in the function of the network. Pathway pages also give information about the other pathways, diseases and phenotypes with which the members of a particular pathway are associated.
The Gene Annotator Tool. Shown in the center of the figure is the menu bar from the Gene Annotator (GA) tool. The default result is the ‘Annotations’ page (top left), which gives detailed lists of annotations for each gene in the input list and its corresponding orthologs, as well as a list of external database identifiers for that gene with links to additional information at the other databases. The ‘Annotation Distribution’ analysis (bottom left) indicates the percentage of genes in the input list associated with lists of disease, pathway, phenotype, biological process, molecular function, cellular component and chemical interaction terms, beginning with the terms that appear most commonly. Selecting a term shows the subset of the input list of genes that are associated with that term or any more specific term beneath it in the ontology. Check boxes allow the user to select multiple terms within one or across multiple ontologies to see the genes with annotations to all the selected terms. This smaller subset of the original list can then be entered into the GA Tool for further analysis. The ‘Comparison Heat Map’ function (top right) allows users to select any two ontologies, or to view the overlap between two branches of the same ontology. In this case, the number of genes from the original input list which are associated with disease categories under ‘Cerebrovascular Disorders’ and pathway categories under ‘signaling pathways’ are shown, with intersections containing a higher number of associated genes displayed as increasingly darker colors. Finally, the ‘Genome Plot’ (bottom right) shows the location of each gene in the list against the full set of chromosomes for the species, in this case, the rat karyotype, with the chromosomal positions for all the genes in the list presented in a table below the image (not shown). Functionality for the Genome Plot is the same as that described earlier for the Genome Viewer tool.
The Rat Genome Database 2009: Variation, Ontologies and Pathways
MR Dwinell et al.
Nucleic Acids Res 37 (Database issue), D744-9.
The Rat Genome Database (RGD, http://rgd.mcw.edu) was developed to provide a core resource for rat researchers combining genetic, genomic, pathway, phenotype and strain i …
Rat Genome Database (RGD): Mapping Disease Onto the Genome
S Twigger et al.
Nucleic Acids Res 30 (1), 125-8.
The Rat Genome Database (RGD, http://rgd.mcw.edu) is an NIH-funded project whose stated mission is 'to collect, consolidate and integrate data generated from ongoing rat …
Exploring Human Disease Using the Rat Genome Database
M Shimoyama et al.
Dis Model Mech 9 (10), 1089-1095.
Rattus norvegicus, the laboratory rat, has been a crucial model for studies of the environmental and genetic factors associated with human diseases for over 150 years. It …
Exploring Genetic, Genomic, and Phenotypic Data at the Rat Genome Database
SJ Laulederkind et al.
Curr Protoc Bioinformatics Chapter 1, Unit1.14.
The laboratory rat, Rattus norvegicus, is an important model of human health and disease, and experimental findings in the rat have relevance to human physiology and dise …
Rat Genome and Model Resources
M Shimoyama et al.
ILAR J 58 (1), 42-58.
Rats remain a major model for studying disease mechanisms and discovery, validation, and testing of new compounds to improve human health. The rat's value continues to gr …
PubMed Central articles
Endothelial Heterogeneity Across Distinct Vascular Beds During Homeostasis and Inflammation
A Jambusaria et al.
Blood vessels are lined by endothelial cells engaged in distinct organ-specific functions but little is known about their characteristic gene expression profiles. RNA-Seq …
Identification of Infectious Disease-Associated Host Genes Using Machine Learning Techniques
RK Barman et al.
BMC Bioinformatics 20 (1), 736.
To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale predi …
Polyphenol Effects on Cholesterol Metabolism via Bile Acid Biosynthesis, CYP7A1: A Review
KF Chambers et al.
Nutrients 11 (11).
Atherosclerosis, the main contributor to coronary heart disease, is characterised by an accumulation of lipids such as cholesterol in the arterial wall. Reverse cholester …
Using MARRVEL v1.2 for Bioinformatics Analysis of Human Genes and Variant Pathogenicity
J Wang et al.
Curr Protoc Bioinformatics 67 (1), e85.
One of the greatest challenges in the bioinformatic analysis of human sequencing data is identifying which variants are pathogenic. Numerous databases and tools have been …
Epigenetic Regulation of Vascular Smooth Muscle Cells by Histone H3 Lysine 9 Dimethylation Attenuates Target Gene-Induction by Inflammatory Signaling
JL Harman et al.
Arterioscler Thromb Vasc Biol 39 (11), 2289-2302.
This study implicates H3K9me2 in regulating the proinflammatory VSMC phenotype. Our findings suggest that reduced H3K9me2 in disease enhance binding of NFκB and AP-1 (act …
Aitman T.J., Critser J.K., Cuppen E., Dominczak A., Fernandez-Suarez X.M., Flint J., Gauguier D., Geurts A.M., Gould M., Harris P.C., et al. Progress and prospects in rat genetics: a community view. Nat. Genet. 2008;40:516–522.
Shimoyama M., Hayman G.T., Laulederkind S.J., Nigam R., Lowry T.F., Petri V., Smith J.R., Wang S.J., Munzenmaier D.H., Twigger S.N., et al. The Rat Genome Database curators: who, what, where, why. PLoS Comput. Biol. 2009;5:e1000582.
Shimoyama M., Smith J.R., Hayman T., Laulederkind S., Lowry T., Nigam R., Petri V., Wang S.J., Dwinell M., Jacob H., et al. RGD: a comparative genomics platform. Hum. Genomics. 2011;5:124–129.
Laulederkind S.J., Hayman G.T., Wang S.J., Smith J.R., Lowry T.F., Nigam R., Petri V., De Pons J., Dwinell M.R., Shimoyama M., et al. The Rat Genome Database 2013—data, tools and users. Brief. Bioinform. 2013;14:520–526.
Wang S.J., Laulederkind S.J., Hayman G.T., Smith J.R., Petri V., Lowry T.F., Nigam R., Dwinell M.R., Worthey E.A., Munzenmaier D.H., et al. Analysis of disease-associated objects at the Rat Genome Database. Database (Oxford) 2013;2013:bat046.
Research Support, N.I.H., Extramural
Molecular Sequence Annotation