The Rat Genome Database (RGD) facilitates genomic and phenotypic data integration across multiple species for biomedical research.

Model organism research is essential for discovering the mechanisms of human diseases by defining biologically meaningful gene to disease relationships. The Rat Genome Database (RGD, ( https://rgd.mcw.edu )) is a cross-species knowledgebase and the premier online resource for rat genetic and physiologic data. This rich resource is enhanced by the inclusion and integration of comparative data for human and mouse, as well as other human disease models including chinchilla, dog, bonobo, pig, 13-lined ground squirrel, green monkey, and naked mole-rat. Functional information has been added to records via the assignment of annotations based on sequence similarity to human, rat, and mouse genes. RGD has also imported well-supported cross-species data from external resources. To enable use of these data, RGD has developed a robust infrastructure of standardized ontologies, data formats, and disease- and species-centric portals, complemented with a suite of innovative tools for discovery and analysis. Using examples of single-gene and polygenic human diseases, we illustrate how data from multiple species can help to identify or confirm a gene as involved in a disease and to identify model organisms that can be studied to understand the pathophysiology of a gene or pathway. The ultimate aim of this report is to demonstrate the utility of RGD not only as the core resource for the rat research community but also as a source of bioinformatic tools to support a wider audience, empowering the search for appropriate models for human afflictions.

The Year of the Rat: The Rat Genome Database at 20: a multi-species knowledgebase and analysis platform.

Formed in late 1999, the Rat Genome Database (RGD, https://rgd.mcw.edu) will be 20 in 2020, the Year of the Rat. Because the laboratory rat, Rattus norvegicus, has been used as a model for complex human diseases such as cardiovascular disease, diabetes, cancer, neurological disorders and arthritis, among others, for >150 years, RGD has always been disease-focused and committed to providing data and tools for researchers doing comparative genomics and translational studies. At its inception, before the sequencing of the rat genome, RGD started with only a few data types localized on genetic and radiation hybrid (RH) maps and offered only a few tools for querying and consolidating that data. Since that time, RGD has expanded to include a wealth of structured and standardized genetic, genomic, phenotypic, and disease-related data for eight species, and a suite of innovative tools for querying, analyzing and visualizing this data. This article provides an overview of recent substantial additions and improvements to RGD's data and tools that can assist researchers in finding and utilizing the data they need, whether their goal is to develop new precision models of disease or to more fully explore emerging details within a system or across multiple systems.

The United Kingdom Primary Immune Deficiency (UKPID) registry 2012 to 2017.

This is the second report of the United Kingdom Primary Immunodeficiency (UKPID) registry. The registry will be a decade old in 2018 and, as of August 2017, had recruited 4758 patients encompassing 97% of immunology centres within the United Kingdom. This represents a doubling of recruitment into the registry since we reported on 2229 patients included in our first report of 2013. Minimum PID prevalence in the United Kingdom is currently 5·90/100 000 and an average incidence of PID between 1980 and 2000 of 7·6 cases per 100 000 UK live births. Data are presented on the frequency of diseases recorded, disease prevalence, diagnostic delay and treatment modality, including haematopoietic stem cell transplantation (HSCT) and gene therapy. The registry provides valuable information to clinicians, researchers, service commissioners and industry alike on PID within the United Kingdom, which may not otherwise be available without the existence of a well-established registry.

The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease.

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.

Comprehensive coverage of cardiovascular disease data in the disease portals at the Rat Genome Database.

Cardiovascular diseases are complex diseases caused by a combination of genetic and environmental factors. To facilitate progress in complex disease research, the Rat Genome Database (RGD) provides the community with a disease portal where genome objects and biological data related to cardiovascular diseases are systematically organized. The purpose of this study is to present biocuration at RGD, including disease, genetic, and pathway data. The RGD curation team uses controlled vocabularies/ontologies to organize data curated from the published literature or imported from disease and pathway databases. These organized annotations are associated with genes, strains, and quantitative trait loci (QTLs), thus linking functional annotations to genome objects. Screen shots from the web pages are used to demonstrate the organization of annotations at RGD. The human cardiovascular disease genes identified by annotations were grouped according to data sources and their annotation profiles were compared by in-house tools and other enrichment tools available to the public. The analysis results show that the imported cardiovascular disease genes from ClinVar and OMIM are functionally different from the RGD manually curated genes in terms of pathway and Gene Ontology annotations. The inclusion of disease genes from other databases enriches the collection of disease genes not only in quantity but also in quality.

The Rat Genome Database 2013--data, tools and users.

The Rat Genome Database (RGD) was started >10 years ago to provide a core genomic resource for rat researchers. Currently, RGD combines genetic, genomic, pathway, phenotype and strain information with a focus on disease. RGD users are provided with access to structured and curated data from the molecular level through the organismal level. Those users access RGD from all over the world. End users are not only rat researchers but also researchers working with mouse and human data. Translational research is supported by RGD's comparative genetics/genomics data in disease portals, in GBrowse, in VCMap and on gene report pages. The impact of RGD also goes beyond the traditional biomedical researcher, as the influence of RGD reaches bioinformaticians, tool developers and curators. Import of RGD data into other publicly available databases expands the influence of RGD to a larger set of end users than those who avail themselves of the RGD website. The value of RGD continues to grow as more types of data and more tools are added, while reaching more types of end users.

Disease pathways at the Rat Genome Database Pathway Portal: genes in context-a network approach to understanding the molecular mechanisms of disease.

BACKGROUND: Biological systems are exquisitely poised to respond and adjust to challenges, including damage. However, sustained damage can overcome the ability of the system to adjust and result in a disease phenotype, its underpinnings many times elusive. Unraveling the molecular mechanisms of systems biology, of how and why it falters, is essential for delineating the details of the path(s) leading to the diseased state and for designing strategies to revert its progression. An important aspect of this process is not only to define the function of a gene but to identify the context within which gene functions act. It is within the network, or pathway context, that the function of a gene fulfills its ultimate biological role. Resolving the extent to which defective function(s) affect the proceedings of pathway(s) and how altered pathways merge into overpowering the system's defense machinery are key to understanding the molecular aspects of disease and envisioning ways to counteract it. A network-centric approach to diseases is increasingly being considered in current research. It also underlies the deployment of disease pathways at the Rat Genome Database Pathway Portal. The portal is presented with an emphasis on disease and altered pathways, associated drug pathways, pathway suites, and suite networks. RESULTS: The Pathway Portal at the Rat Genome Database (RGD) provides an ever-increasing collection of interactive pathway diagrams and associated annotations for metabolic, signaling, regulatory, and drug pathways, including disease and altered pathways. A disease pathway is viewed from the perspective of networks whose alterations are manifested in the affected phenotype. The Pathway Ontology (PW), built and maintained at RGD, facilitates the annotations of genes, the deployment of pathway diagrams, and provides an overall navigational tool. Pathways that revolve around a common concept and are globally connected are presented within pathway suites; a suite network combines two or more pathway suites. CONCLUSIONS: The Pathway Portal is a rich resource that offers a range of pathway data and visualization, including disease pathways and related pathway suites. Viewing a disease pathway from the perspective of underlying altered pathways is an aid for dissecting the molecular mechanisms of disease.

The utility of the ISAC allergen array in the investigation of idiopathic anaphylaxis.

A diagnosis of idiopathic anaphylaxis following a detailed clinical assessment remains very challenging for patients and clinicians. Risk reduction strategies such as allergen avoidance are not possible. This study investigated whether the (ISAC) allergen array with 103 allergens would add diagnostic value in patients with idiopathic anaphylaxis. We extended the specific immunoglobulin (Ig)E testing in 110 patients with a diagnosis of idiopathic anaphylaxis from five UK specialist centres using ISAC arrays. These were divided into three groups: score I identified no new allergen sensitization beyond those known by previous assessment, score II identified new sensitizations which were not thought likely to explain the anaphylaxis and score III identified new sensitizations felt to have a high likelihood of being responsible for the anaphylaxis. A proportion (50%) of score III patients underwent clinical reassessment to substantiate the link to anaphylaxis in this group. The results show that 20% of the arrays were classified as score III with a high likelihood of identifying the cause of the anaphylaxis. A wide range of major allergens were identified, the most frequent being omega-5-gliadin and shrimp, together accounting for 45% of the previously unrecognized sensitizations. The ISAC array contributed to the diagnosis in 20% of patients with idiopathic anaphylaxis. It may offer additional information where a careful allergy history and follow-on testing have not revealed the cause of the anaphylaxis.

The United Kingdom Primary Immune Deficiency (UKPID) Registry: report of the first 4 years' activity 2008-2012.

This report summarizes the establishment of the first national online registry of primary immune deficency in the United Kingdom, the United Kingdom Primary Immunodeficiency (UKPID Registry). This UKPID Registry is based on the European Society for Immune Deficiency (ESID) registry platform, hosted on servers at the Royal Free site of University College, London. It is accessible to users through the website of the United Kingdom Primary Immunodeficiency Network (www.ukpin.org.uk). Twenty-seven centres in the United Kingdom are actively contributing data, with an additional nine centres completing their ethical and governance approvals to participate. This indicates that 36 of 38 (95%) of recognized centres in the United Kingdom have engaged with this project. To date, 2229 patients have been enrolled, with a notable increasing rate of recruitment in the past 12 months. Data are presented on the range of diagnoses recorded, estimated minimum disease prevalence, geographical distribution of patients across the United Kingdom, age at presentation, diagnostic delay, treatment modalities used and evidence of their monitoring and effectiveness.

The updated RGD Pathway Portal utilizes increased curation efficiency and provides expanded pathway information.

The RGD Pathway Portal provides pathway annotations for rat, human and mouse genes and pathway diagrams and suites, all interconnected via the pathway ontology. Diagram pages present the diagram and description, with diagram objects linked to additional resources. A newly-developed dual-functionality web application composes the diagram page. Curators input the description, diagram, references and additional pathway objects. The application combines these with tables of rat, human and mouse pathway genes, including genetic information, analysis tool and reference links, and disease, phenotype and other pathway annotations to pathway genes. The application increases the information content of diagram pages while expediting publication.

Rat Genome Database: a unique resource for rat, human, and mouse quantitative trait locus data.

The rat has been widely used as a disease model in a laboratory setting, resulting in an abundance of genetic and phenotype data from a wide variety of studies. These data can be found at the Rat Genome Database (RGD, http://rgd.mcw.edu/), which provides a platform for researchers interested in linking genomic variations to phenotypes. Quantitative trait loci (QTLs) form one of the earliest and core datasets, allowing researchers to identify loci harboring genes associated with disease. These QTLs are not only important for those using the rat to identify genes and regions associated with disease, but also for cross-organism analyses of syntenic regions on the mouse and the human genomes to identify potential regions for study in these organisms. Currently, RGD has data on >1,900 rat QTLs that include details about the methods and animals used to determine the respective QTL along with the genomic positions and markers that define the region. RGD also curates human QTLs (>1,900) and houses>4,000 mouse QTLs (imported from Mouse Genome Informatics). Multiple ontologies are used to standardize traits, phenotypes, diseases, and experimental methods to facilitate queries, analyses, and cross-organism comparisons. QTLs are visualized in tools such as GBrowse and GViewer, with additional tools for analysis of gene sets within QTL regions. The QTL data at RGD provide valuable information for the study of mapped phenotypes and identification of candidate genes for disease associations.

The Rat Genome Database: Genetic, Genomic, and Phenotypic Data Across Multiple Species.

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 disease. The Rat Genome Database (RGD, https://rgd.mcw.edu) is a model organism database that provides access to a wide variety of curated rat data including disease associations, phenotypes, pathways, molecular functions, biological processes, cellular components, and chemical interactions for genes, quantitative trait loci, and strains. We present an overview of the database followed by specific examples that can be used to gain experience in employing RGD to explore the wealth of functional data available for the rat and other species. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Navigating the Rat Genome Database (RGD) home page Basic Protocol 2: Using the RGD search functions Basic Protocol 3: Searching for quantitative trait loci Basic Protocol 4: Using the RGD genome browser (JBrowse) to find phenotypic annotations Basic Protocol 5: Using OntoMate to find gene-disease data Basic Protocol 6: Using MOET to find gene-ontology enrichment Basic Protocol 7: Using OLGA to generate gene lists for analysis Basic Protocol 8: Using the GA tool to analyze ontology annotations for genes Basic Protocol 9: Using the RGD InterViewer tool to find protein interaction data Basic Protocol 10: Using the RGD Variant Visualizer tool to find genetic variant data Basic Protocol 11: Using the RGD Disease Portals to find disease, phenotype, and other information Basic Protocol 12: Using the RGD Phenotypes & Models Portal to find qualitative and quantitative phenotype data and other rat strain-related information Basic Protocol 13: Using the RGD Pathway Portal to find disease and phenotype data via molecular pathways.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource.

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

Rare disease research resources at the Rat Genome Database.

Rare diseases individually affect relatively few people, but as a group they impact considerable numbers of people. The Rat Genome Database (https://rgd.mcw.edu) is a knowledgebase that offers resources for rare disease research. This includes disease definitions, genes, quantitative trail loci (QTLs), genetic variants, annotations to published literature, links to external resources, and more. One important resource is identifying relevant cell lines and rat strains that serve as models for disease research. Diseases, genes, and strains have report pages with consolidated data, and links to analysis tools. Utilizing these globally accessible resources for rare disease research, potentiating discovery of mechanisms and new treatments, can point researchers toward solutions to alleviate the suffering of those afflicted with these diseases.

Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal.

The COVID-19 pandemic stemmed a parallel upsurge in the scientific literature about SARS-CoV-2 infection and its health burden. The Rat Genome Database (RGD) created a COVID-19 Disease Portal to leverage information from the scientific literature. In the COVID-19 Portal, gene-disease associations are established by manual curation of PubMed literature. The portal contains data for nine ontologies related to COVID-19, an embedded enrichment analysis tool, as well as links to a toolkit. Using these information and tools, we performed analyses on the curated COVID-19 disease genes. As expected, Disease Ontology enrichment analysis showed that the COVID-19 gene set is highly enriched with coronavirus infectious disease and related diseases. However, other less related diseases were also highly enriched, such as liver and rheumatic diseases. Using the comparison heatmap tool, we found nearly 60 percent of the COVID-19 genes were associated with nervous system disease and 40 percent were associated with gastrointestinal disease. Our analysis confirms the role of the immune system in COVID-19 pathogenesis as shown by substantial enrichment of immune system related Gene Ontology terms. The information in RGD's COVID-19 disease portal can generate new hypotheses to potentiate novel therapies and prevention of acute and long-term complications of COVID-19.

MOET: a web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses.

Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html.

NECo: A node embedding algorithm for multiplex heterogeneous networks.

Complex diseases such as hypertension, cancer, and diabetes cause nearly 70% of the deaths in the U.S. and involve multiple genes and their interactions with environmental factors. Therefore, identification of genetic factors to understand and decrease the morbidity and mortality from complex diseases is an important and challenging task. With the generation of an unprecedented amount of multi-omics datasets, network-based methods have become popular to represent the multilayered complex molecular interactions. Particularly node embeddings, the low-dimensional representations of nodes in a network are utilized for gene function prediction. Integrated network analysis of multi-omics data alleviates the issues related to missing data and lack of context-specific datasets. Most of the node embedding methods, however, are unable to integrate multiple types of datasets from genes and phenotypes. To address this limitation, we developed a node embedding algorithm called Node Embeddings of Complex networks (NECo) that can utilize multilayered heterogeneous networks of genes and phenotypes. We evaluated the performance of NECo using genotypic and phenotypic datasets from rat (Rattus norvegicus) disease models to classify hypertension disease-related genes. Our method significantly outperformed the state-of-the-art node embedding methods, with AUC of 94.97% compared 85.98% in the second-best performer, and predicted genes not previously implicated in hypertension.

Improving the quality of maternal and newborn care in the Pacific region: A scoping review.

BACKGROUND: Quality care is essential for improving maternal and newborn health. Low- and middle-income Pacific Island nations face challenges in delivering quality maternal and newborn care. The aim of this review was to identify all published studies of interventions which sought to improve the quality of maternal and newborn care in Pacific low-and middle-income countries. METHODS: A scoping review framework was used. Databases and grey literature were searched for studies published between January 2000 and July 2019 which described actions to improve the quality of maternal and newborn care in Pacific low- and middle-income countries. Interventions were categorised using a four-level health system framework and the WHO quality of maternal and newborn care standards. An expert advisory group of Pacific Islander clinicians and researchers provided guidance throughout the review process. RESULTS: 2010 citations were identified and 32 studies included. Most interventions focused on the clinical service or organisational level, such as healthcare worker training, audit processes and improvements to infrastructure. Few addressed patient experiences or system-wide improvements. Enablers to improving quality care included community engagement, collaborative partnerships, adequate staff education and training and alignment with local priorities. CONCLUSIONS: There are several quality improvement initiatives in low- and middle-income Pacific Island nations, most at the point of health service delivery. To effectively strengthen quality maternal and newborn care in this region, efforts must broaden to improve health system leadership, deliver sustaining education programs and encompass learnings from women and their communities.

Graves disease associated with chronic idiopathic urticaria: 2 case reports.

Chronic idiopathic urticaria (CIU) is well known to be associated with antithyroid peroxidase antibodies and autoimmune thyroiditis. Coexisting Graves disease has only rarely been observed. We describe 2 patients with CIU who developed autoimmune hyperthyroidism with antithyrotropin receptor antibodies. Antithyroid peroxidase antibodies were also present in 1 of the patients, but both responded poorly to high-dose antihistamine therapy. Both patients improved significantly, and their thyroid function recovered with carbimazole. We advise clinicians to be alert to the symptoms of hyperthyroidism when patients with CIU respond poorly to antihistamine therapy, as prompt treatment of hyperthyroidism significantly improves urticaria.

Predicting instances of pathway ontology classes for pathway integration.

BACKGROUND: To improve the outcomes of biological pathway analysis, a better way of integrating pathway data is needed. Ontologies can be used to organize data from disparate sources, and we leverage the Pathway Ontology as a unifying ontology for organizing pathway data. We aim to associate pathway instances from different databases to the appropriate class in the Pathway Ontology. RESULTS: Using a supervised machine learning approach, we trained neural networks to predict mappings between Reactome pathways and Pathway Ontology (PW) classes. For 2222 Reactome classes, the neural network (NN) model generated 10,952 class recommendations. We compared against a baseline bag-of-words (BOW) model for predicting correct PW classes. A 5% subset of Reactome pathways (111 pathways) was randomly selected, and the corresponding class recommendations from both models were evaluated by two curators. The precision of the BOW model was higher (0.49 for BOW and 0.39 for NN), but the recall was lower (0.42 for BOW and 0.78 for NN). Around 78% of Reactome pathways received pertinent recommendations from the NN model. CONCLUSIONS: The neural predictive model produced meaningful class recommendations that assisted PW curators in selecting appropriate class mappings for Reactome pathways. Our methods can be used to reduce the manual effort associated with ontology curation, and more broadly, for augmenting the curators' ability to organize and integrate data from pathway databases using the Pathway Ontology.