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, 43 (Database issue), D743-50

The Rat Genome Database 2015: Genomic, Phenotypic and Environmental Variations and Disease

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The Rat Genome Database 2015: Genomic, Phenotypic and Environmental Variations and Disease

Mary Shimoyama et al. Nucleic Acids Res.

Abstract

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.

Figures

Figure 1.
Figure 1.
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.
Figure 2.
Figure 2.
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.
Figure 3.
Figure 3.
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.
Figure 4.
Figure 4.
(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.
Figure 5.
Figure 5.
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.
Figure 6.
Figure 6.
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.

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