Using Multiple Ontologies to Integrate Complex Biological Data

The strength of the rat as a model organism lies in its utility in pharmacology, biochemistry and physiology research. Data resulting from such studies is difficult to represent in databases and the creation of user-friendly data mining tools has proved difficult. The Rat Genome Database has developed a comprehensive ontology-based data structure and annotation system to integrate physiological data along with environmental and experimental factors, as well as genetic and genomic information. RGD uses multiple ontologies to integrate complex biological information from the molecular level to the whole organism, and to develop data mining and presentation tools. This approach allows RGD to indicate not only the phenotypes seen in a strain but also the specific values under each diet and atmospheric condition, as well as gender differences. Harnessing the power of ontologies in this way allows the user to gather and filter data in a customized fashion, so that a researcher can retrieve all phenotype readings for which a high hypoxia is a factor. Utilizing the same data structure for expression data, pathways and biological processes, RGD will provide a comprehensive research platform which allows users to investigate the conditions under which biological processes are altered and to elucidate the mechanisms of disease

The Rat Genome Database (RGD): developments towards a phenome database

The Rat Genome Database (RGD) (http://rgd.mcw.edu) aims to meet the needs of its community by providing genetic and genomic infrastructure while also annotating the strengths of rat research: biochemistry, nutrition, pharmacology and physiology. Here, we report on RGD's development towards creating a phenome database. Recent developments can be categorized into three groups. (i) Improved data collection and integration to match increased volume and biological scope of research. (ii) Knowledge representation augmented by the implementation of a new ontology and annotation system. (iii) The addition of quantitative trait loci data, from rat, mouse and human to our advanced comparative genomics tools, as well as the creation of new, and enhancement of existing, tools to enable users to efficiently browse and survey research data. The emphasis is on helping researchers find genes responsible for disease through the use of rat models. These improvements, combined with the genomic sequence of the rat, have led to a successful year at RGD with over two million page accesses that represent an over 4-fold increase in a year. Future plans call for increased annotation of biological information on the rat elucidated through its use as a model for human pathobiology. The continued development of toolsets will facilitate integration of these data into the context of rat genomic sequence, as well as allow comparisons of biological and genomic data with the human genomic sequence and of an increasing number of organisms

Rat Genome Database (RGD): mapping disease onto the genome

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

Integrative Genomics: In Silico Coupling of Rat Physiology and Complex Traits With Mouse and Human Data

Integration of the large variety of genome maps from several organisms provides the mechanism by which physiological knowledge obtained in model systems such as the rat can be projected onto the human genome to further the research on human disease. The release of the rat genome sequence provides new information for studies using the rat model and is a key reference against which existing and new rat physiological results can be aligned. Previously, we described comparative maps of the rat, mouse, and human based on EST sequence comparisons combined with radiation hybrid maps. Here, we use new data and introduce the Integrated Genomics Environment, an extensive database of curated and integrated maps, markers, and physiological results. These results are integrated by using VCMapview, a java-based map integration and visualization tool. This unique environment allows researchers to relate results from cytogenetic, genetic, and radiation hybrid studies to the genome sequence and compare regions of interest between human, mouse, and rat. Integrating rat physiology with mouse genetics and clinical results from human by using the respective genomes provides a novel route to capitalize on comparative genomics and the strengths of model organism biology

High-Density Rat Radiation Hybrid Maps Containing Over 24,000 SSLPs, Genes, and ESTs Provide a Direct Link to the Rat Genome Sequence

The laboratory rat is a major model organism for systems biology. To complement the cornucopia of physiological and pharmacological data generated in the rat, a large genomic toolset has been developed, culminating in the release of the rat draft genome sequence. The rat draft sequence used a variety of assembly packages, as well as data from the Radiation Hybrid (RH) map of the rat as part of their validation. As part of the Rat Genome Project, we have been building a high-density RH map to facilitate data integration from multiple maps and now to help validate the genome assembly. By incorporating vectors from our lab and several other labs, we have doubled the number of simple sequence length polymorphisms (SSLPs), genes, expressed sequence tags (ESTs), and sequence-tagged sites (STSs) compared to any other genome-wide rat map, a total of 24,437 elements. During the process, we also identified a novel approach for integrating the RH placement results from multiple maps. This new integrated RH map contains approximately 10 RH-mapped elements per Mb on the genome assembly, enabling the RH maps to serve as a scaffold for a variety of data visualization tools

Effect of ecological group classification schemes on performance of the AMBI benthic index in US coastal waters

The AZTI Marine Biotic Index (AMBI) requires less geographically-specific calibration than other benthic indices, but has not performed as well in US coastal waters as it has in the European waters for which it was originally developed. Here we examine the extent of improvement in index performance when the Ecological Group (EG) classifications on which AMBI is based are derived using local expertise. Twenty-three US benthic experts developed EG scores for each of three regions in the United States, as well as for the US as a whole. Index performance was then compared using: (1) EG scores specific to a region, (2) national EG scores, (3) national EG scores supplemented with standard international EG scores for taxa that the US experts were not able to make assignments, and (4) standard international EG scores. Performance of each scheme was evaluated by diagnosis of condition at pre-defined good/bad sites, concordance with existing local benthic indices, and independence from natural environmental gradients. The AMBI performed best when using the national EG assignments augmented with standard international EG values. The AMBI using this hybrid EG scheme performed well in differentiating apriori good and bad sites (>80% correct classification rate) and AMBI scores were both concordant and correlated (rs = 0.4–0.7) with those of existing local indices. Nearly all of the results suggest that assigning the EG values in the framework of local biogeographic conditions produced a better-performing version of AMBI. The improved index performance, however, was tempered with apparent biases in score distribution. The AMBI, regardless of EG scheme, tended to compress ratings away from the extremes and toward the moderate condition and there was a bias with salinity, where high quality sites received increasingly poorer condition scores with decreasing salinity

Effect of Ecological Group Classification Schemes on Performance of the AMBI Benthic Index in US Coastal Waters

The AZTI Marine Biotic Index (AMBI) requires less geographically-specific calibration than other benthic indices, but has not performed as well in US coastal waters as it has in the European waters for which it was originally developed. Here we examine the extent of improvement in index performance when the Ecological Group (EG) classifications on which AMBI is based are derived using local expertise. Twenty-three US benthic experts developed EG scores for each of three regions in the United States, as well as for the US as a whole. Index performance was then compared using: (1) EG scores specific to a region, (2) national EG scores, (3) national EG scores supplemented with standard international EG scores for taxa that the US experts were not able to make assignments, and (4) standard international EG scores. Performance of each scheme was evaluated by diagnosis of condition at pre-defined good/bad sites, concordance with existing local benthic indices, and independence from natural environmental gradients. The AMBI performed best when using the national EG assignments augmented with standard international EG values. The AMBI using this hybrid EG scheme performed well in differentiating apriori good and bad sites (\u3e80% correct classification rate) and AMBI scores were both concordant and correlated (rs = 0.4–0.7) with those of existing local indices. Nearly all of the results suggest that assigning the EG values in the framework of local biogeographic conditions produced a better-performing version of AMBI. The improved index performance, however, was tempered with apparent biases in score distribution. The AMBI, regardless of EG scheme, tended to compress ratings away from the extremes and toward the moderate condition and there was a bias with salinity, where high quality sites received increasingly poorer condition scores with decreasing salinity