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Analysis of disease-associated objects at the Rat Genome Database.

ABSTRACT: The Rat Genome Database (RGD) is the premier resource for genetic, genomic and phenotype data for the laboratory rat, Rattus norvegicus. In addition to organizing biological data from rats, the RGD team focuses on manual curation of gene-disease associations for rat, human and mouse. In this work, we have analyzed disease-associated strains, quantitative trait loci (QTL) and genes from rats. These disease objects form the basis for seven disease portals. Among disease portals, the cardiovascular disease and obesity/metabolic syndrome portals have the highest number of rat strains and QTL. These two portals share 398 rat QTL, and these shared QTL are highly concentrated on rat chromosomes 1 and 2. For disease-associated genes, we performed gene ontology (GO) enrichment analysis across portals using RatMine enrichment widgets. Fifteen GO terms, five from each GO aspect, were selected to profile enrichment patterns of each portal. Of the selected biological process (BP) terms, 'regulation of programmed cell death' was the top enriched term across all disease portals except in the obesity/metabolic syndrome portal where 'lipid metabolic process' was the most enriched term. 'Cytosol' and 'nucleus' were common cellular component (CC) annotations for disease genes, but only the cancer portal genes were highly enriched with 'nucleus' annotations. Similar enrichment patterns were observed in a parallel analysis using the DAVID functional annotation tool. The relationship between the preselected 15 GO terms and disease terms was examined reciprocally by retrieving rat genes annotated with these preselected terms. The individual GO term-annotated gene list showed enrichment in physiologically related diseases. For example, the 'regulation of blood pressure' genes were enriched with cardiovascular disease annotations, and the 'lipid metabolic process' genes with obesity annotations. Furthermore, we were able to enhance enrichment of neurological diseases by combining 'G-protein coupled receptor binding' annotated genes with 'protein kinase binding' annotated genes. Database URL: http://rgd.mcw.edu

SUBMITTER: Wang SJ

PROVIDER: S-EPMC3689439 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Analysis of disease-associated objects at the Rat Genome Database.

Wang Shur-Jen SJ Laulederkind Stanley J F SJ Hayman G T GT Smith Jennifer R JR Petri Victoria V Lowry Timothy F TF Nigam Rajni R Dwinell Melinda R MR Worthey Elizabeth A EA Munzenmaier Diane H DH Shimoyama Mary M Jacob Howard J HJ

Database : the journal of biological databases and curation 20130621

The Rat Genome Database (RGD) is the premier resource for genetic, genomic and phenotype data for the laboratory rat, Rattus norvegicus. In addition to organizing biological data from rats, the RGD team focuses on manual curation of gene-disease associations for rat, human and mouse. In this work, we have analyzed disease-associated strains, quantitative trait loci (QTL) and genes from rats. These disease objects form the basis for seven disease portals. Among disease portals, the cardiovascular ...[more]

PMID: 23794737

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Project description: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 genetic and genomic research efforts and make these data widely available to the scientific community'. In a collaboration between the Bioinformatics Research Center at the Medical College of Wisconsin, the Jackson Laboratory and the National Center for Biotechnology Information, RGD has been created to meet these stated aims. The rat is uniquely suited to its role as a model of human disease and the primary focus of RGD is to aid researchers in their study of the rat and in applying their results to studies in a wider context. In support of this we have integrated a large amount of rat genetic and genomic resources in RGD and these are constantly being expanded through ongoing literature and bulk dataset curation. RGD version 2.0, released in June 2001, includes curated data on rat genes, quantitative trait loci (QTL), microsatellite markers and rat strains used in genetic and genomic research. VCMap, a dynamic sequence-based homology tool was introduced, and allows researchers of rat, mouse and human to view mapped genes and sequences and their locations in the other two organisms, an essential tool for comparative genomics. In addition, RGD provides tools for gene prediction, radiation hybrid mapping, polymorphic marker selection and more. Future developments will include the introduction of disease-based curation expanding the curated information to cover popular disease systems studied in the rat. This will be integrated with the emerging rat genomic sequence and annotation pipelines to provide a high-quality disease-centric resource, applicable to human and mouse via comparative tools such as VCMap. RGD has a defined community outreach focus with a Visiting Scientist program and the Rat Community Forum, a web-based forum for rat researchers and others interested in using the rat as an experimental model. Thus, RGD is not only a valuable resource for those working with the rat but also for researchers in other model organisms wishing to harness the existing genetic and physiological data available in the rat to complement their own work.

Project description:The databases included on this article refers to variables and parameters belonging to the Space Traffic Management (STM), Evidence Theory and Machine Learning (ML) fields. They have been used for implementing ML for autonomously predict risk associated to a close encounter between two space (Sanchez and Vasile, On the Use of Machine Learning and Evidence Theory to Improve Collision Risk Management, Acta Astronautica, Special Issue for ICSSA2020, In Press [1]). The position of the objected is assumed to be affected by epistemic uncertainty, which has been modeled according to Dempster-Shafer Evidence theory (DSt) [2]. Six datasets are presented. Two (DB1 and DB2, respectively) include samples of space object close encounters subject to epistemic uncertainty on the relative position. Other two databases (DB3 and DB4, respectively) include the values of the Cumulative Plausibility and Belief Curves (CPC and CBC, respectively) of each sample included in DB1. The remaining databases (DB5 and DB6), contain the value of the CPC and CBC of each sample included in DB2. All of them are synthetic databases created using computer simulation to obtain the results presented in [1]. DB1 database is constituted by 9,000 samples and 45 columns and a header, while DB2 is formed by 28,800 samples and 45 columns and a header. These databases come from a set of, respectively, 5 and 14 different families of encounter geometries defined by the range of values that can be assigned to the bounds of the intervals for the uncertain variables, assumed to be affected by epistemic uncertainty, considered to have been provided by two sources of information. The uncertain variables are: the miss distance, [µx, µy], on the impact plane (B plane), the standard deviation of the relative position projected on the B plane, [?x, ?y], and the Hard Body Radius of the combined objects, HBR. The dataset is completed with STM related parameters: miss distance and covariance matrix of the uncertain ellipse projected on the B plane enclosing all samples defined by the uncertainty intervals, the Probability of Collision (PC ) of this ellipse or the elapsed time to the Time of Closest Approach (TCA); with DSt related parameters: Belief and Plausibility of certain values of Pc; and the class of the event according to the classification detailed in [1]. DB3 and DB4 are constituted by 34 columns and 9000 rows containing the Plausibility and Belief for Pc values and the corresponding Probabilities of Collision necessary to build the CPC and CBC of the events in DB1, while DB5 and DB6 are constituted by 34 columns and 28,800 rows containing the Plausibility and Belief for Pc values and the corresponding Probabilities of Collision values necessary to build the CPC and CBC of the events in DB2. These databases have a potential usage by the ML community interested in STM as well as for the space community, especially, space operators interested in introduce epistemic uncertainty on collision risk assessment. These databases contribute to build a scarce field such as the databases of encounter events [3].

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Analysis of disease-associated objects at the Rat Genome Database.

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Analysis of disease-associated objects at the Rat Genome Database.

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