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Laulederkind SJF, Hayman GT, Wang SJ, Kaldunski ML, Vedi M, Demos WM, Tutaj M, Smith JR, Lamers L, Gibson AC, Thorat K, Thota J, Tutaj MA, De Pons JL, Dwinell MR, Kwitek AE. The Rat Genome Database: Genetic, Genomic, and Phenotypic Data Across Multiple Species. Curr Protoc 2023; 3:e804. [PMID: 37347557 PMCID: PMC10335880 DOI: 10.1002/cpz1.804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
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.
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Affiliation(s)
| | - G. Thomas Hayman
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Shur-Jen Wang
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Mary L. Kaldunski
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Mahima Vedi
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Wendy M. Demos
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Monika Tutaj
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jennifer R. Smith
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Logan Lamers
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Adam C. Gibson
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ketaki Thorat
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jyothi Thota
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Marek A. Tutaj
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jeffrey L. De Pons
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Melinda R. Dwinell
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Anne E. Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
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Isor A, O'Dea AT, Grady SF, Petroff JT, Skubic KN, Aziz B, Arnatt CK, McCulla RD. Effects of photodeoxygenation on cell biology using dibenzothiophene S-oxide derivatives as O( 3P)-precursors. Photochem Photobiol Sci 2021; 20:1621-1633. [PMID: 34822125 DOI: 10.1007/s43630-021-00136-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/08/2021] [Indexed: 11/29/2022]
Abstract
Photodeoxygenation of dibenzothiophene S-oxide and its derivatives have been used to generate atomic oxygen [O(3P)] to examine its effect on proteins, nucleic acids, and lipids. The unique reactivity and selectivity of O(3P) have shown distinct oxidation products and outcomes in biomolecules and cell-based studies. To understand the scope of its global impact on the cell, we treated MDA-MB-231 cells with 2,8-diacetoxymethyldibenzothiophene S-oxide and UV-A light to produce O(3P) without targeting a specific cell organelle. Cellular responses to O(3P)-release were analyzed using cell viability and cell cycle phase determination assays. Cell death was observed when cells were treated with higher concentrations of sulfoxides and UV-A light. However, significant differences in cell cycle phases due to UV-A irradiation of the sulfoxide were not observed. We further performed RNA-Seq analysis to study the underlying biological processes at play, and while UV-irradiation itself influenced gene expression, there were 9 upregulated and 8 downregulated genes that could be attributed to photodeoxygenation.
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Affiliation(s)
- Ankita Isor
- Department of Chemistry, Saint Louis University, 3501 Laclede Ave., St. Louis, MO, 63103, USA
| | - Austin T O'Dea
- Department of Chemistry, Saint Louis University, 3501 Laclede Ave., St. Louis, MO, 63103, USA
| | - Scott F Grady
- Department of Chemistry, Saint Louis University, 3501 Laclede Ave., St. Louis, MO, 63103, USA
| | - John T Petroff
- Department of Chemistry, Saint Louis University, 3501 Laclede Ave., St. Louis, MO, 63103, USA
| | - Kristin N Skubic
- Department of Chemistry, Saint Louis University, 3501 Laclede Ave., St. Louis, MO, 63103, USA
| | - Bashar Aziz
- Department of Chemistry, Saint Louis University, 3501 Laclede Ave., St. Louis, MO, 63103, USA
| | - Christopher K Arnatt
- Department of Chemistry, Saint Louis University, 3501 Laclede Ave., St. Louis, MO, 63103, USA
| | - Ryan D McCulla
- Department of Chemistry, Saint Louis University, 3501 Laclede Ave., St. Louis, MO, 63103, USA.
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Smith JR, Hayman GT, Wang SJ, Laulederkind SJF, Hoffman MJ, Kaldunski ML, Tutaj M, Thota J, Nalabolu HS, Ellanki SLR, Tutaj MA, De Pons JL, Kwitek AE, Dwinell MR, Shimoyama ME. The Year of the Rat: The Rat Genome Database at 20: a multi-species knowledgebase and analysis platform. Nucleic Acids Res 2020; 48:D731-D742. [PMID: 31713623 PMCID: PMC7145519 DOI: 10.1093/nar/gkz1041] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/21/2019] [Accepted: 10/24/2019] [Indexed: 12/13/2022] Open
Abstract
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.
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Affiliation(s)
- Jennifer R Smith
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- To whom correspondence should be addressed. Tel: +1 414 955 8871; Fax: +1 414 955 6595;
| | - G Thomas Hayman
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Shur-Jen Wang
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Stanley J F Laulederkind
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Matthew J Hoffman
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Genomic Sciences and Precision Medicine Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Mary L Kaldunski
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Monika Tutaj
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jyothi Thota
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Harika S Nalabolu
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Santoshi L R Ellanki
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Marek A Tutaj
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jeffrey L De Pons
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Anne E Kwitek
- Genomic Sciences and Precision Medicine Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Melinda R Dwinell
- Genomic Sciences and Precision Medicine Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Mary E Shimoyama
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Wachira J, Hughes-Darden C, Nkwanta A. Investigating Cell Signaling with Gene Expression Datasets. COURSESOURCE 2019; 6:10.24918/cs.2019.1. [PMID: 32855998 PMCID: PMC7449260 DOI: 10.24918/cs.2019.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Modern molecular biology is a data- and computationally-intensive field with few instructional resources for introducing undergraduate students to the requisite skills and techniques for analyzing large data sets. This Lesson helps students: (i) build an understanding of the role of signal transduction in the control of gene expression; (ii) improve written scientific communication skills through engagement in literature searches, data analysis, and writing reports; and (iii) develop an awareness of the procedures and protocols for analyzing and making inferences from high-content quantitative molecular biology data. The Lesson is most suited to upper level biology courses because it requires foundational knowledge on cellular organization, protein structure and function, and the tenets of information flow from DNA to proteins. The first step lays the foundation for understanding cell signaling, which can be accomplished through assigned readings and presentations. In subsequent active learning sessions, data analysis is integrated with exercises that provide insight into the structure of scientific papers. The Lesson emphasizes the role of quantitative methods in research and helps students gain experience with functional genomics databases and data analysis, which are important skills for molecular biologists. Assessment is conducted through mini-reports designed to gauge students' perceptions of the purpose of each step, their awareness of the possible limitations of the methods utilized, and the ability to identify opportunities for further investigation. Summative assessment is conducted through a final report. The modules are suitable for complementing wet-laboratory experiments and can be adapted for different courses that use molecular biology data.
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Affiliation(s)
- James Wachira
- Department of Biology, Morgan State University, 1700 E. Cold Spring Lane, Baltimore, MD 21251
| | - Cleo Hughes-Darden
- Department of Biology, Morgan State University, 1700 E. Cold Spring Lane, Baltimore, MD 21251
| | - Asamoah Nkwanta
- Department of Mathematics, Morgan State University, 1700 E. Cold Spring Lane, Baltimore, MD 21251
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Shimoyama M, Smith JR, Bryda E, Kuramoto T, Saba L, Dwinell M. Rat Genome and Model Resources. ILAR J 2017; 58:42-58. [PMID: 28838068 PMCID: PMC6057551 DOI: 10.1093/ilar/ilw041] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Indexed: 11/25/2022] Open
Abstract
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 grow as indicated by the more than 1.4 million publications (second to human) at PubMed documenting important discoveries using this model. Advanced sequencing technologies, genome modification techniques, and the development of embryonic stem cell protocols ensure the rat remains an important mammalian model for disease studies. The 2004 release of the reference genome has been followed by the production of complete genomes for more than two dozen individual strains utilizing NextGen sequencing technologies; their analyses have identified over 80 million variants. This explosion in genomic data has been accompanied by the ability to selectively edit the rat genome, leading to hundreds of new strains through multiple technologies. A number of resources have been developed to provide investigators with access to precision rat models, comprehensive datasets, and sophisticated software tools necessary for their research. Those profiled here include the Rat Genome Database, PhenoGen, Gene Editing Rat Resource Center, Rat Resource and Research Center, and the National BioResource Project for the Rat in Japan.
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Affiliation(s)
- Mary Shimoyama
- Department of Biomedical Engineering, Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Rat Genome Database, Department of Biomedical Engineering at Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, Missouri. Institute of Laboratory Animals, Graduate School of Medicine, Kyoto University, Kyoto, Japan. Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado. Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jennifer R Smith
- Department of Biomedical Engineering, Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Rat Genome Database, Department of Biomedical Engineering at Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, Missouri. Institute of Laboratory Animals, Graduate School of Medicine, Kyoto University, Kyoto, Japan. Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado. Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Bryda
- Department of Biomedical Engineering, Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Rat Genome Database, Department of Biomedical Engineering at Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, Missouri. Institute of Laboratory Animals, Graduate School of Medicine, Kyoto University, Kyoto, Japan. Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado. Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Takashi Kuramoto
- Department of Biomedical Engineering, Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Rat Genome Database, Department of Biomedical Engineering at Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, Missouri. Institute of Laboratory Animals, Graduate School of Medicine, Kyoto University, Kyoto, Japan. Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado. Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Laura Saba
- Department of Biomedical Engineering, Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Rat Genome Database, Department of Biomedical Engineering at Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, Missouri. Institute of Laboratory Animals, Graduate School of Medicine, Kyoto University, Kyoto, Japan. Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado. Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Melinda Dwinell
- Department of Biomedical Engineering, Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Rat Genome Database, Department of Biomedical Engineering at Marquette University and the Medical College of Wisconsin, Milwaukee, Wisconsin. Department of Veterinary Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, Missouri. Institute of Laboratory Animals, Graduate School of Medicine, Kyoto University, Kyoto, Japan. Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado. Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
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Petri V, Hayman GT, Tutaj M, Smith JR, Laulederkind SJ, Wang SJ, Nigam R, De Pons J, Shimoyama M, Dwinell MR, Worthey EA, Jacob HJ. Disease pathways at the Rat Genome Database Pathway Portal: genes in context-a network approach to understanding the molecular mechanisms of disease. Hum Genomics 2014; 8:17. [PMID: 25265995 PMCID: PMC4191248 DOI: 10.1186/s40246-014-0017-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Accepted: 09/23/2014] [Indexed: 01/18/2023] Open
Abstract
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.
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InterMOD: integrated data and tools for the unification of model organism research. Sci Rep 2014; 3:1802. [PMID: 23652793 PMCID: PMC3647165 DOI: 10.1038/srep01802] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2012] [Accepted: 04/05/2013] [Indexed: 11/26/2022] Open
Abstract
Model organisms are widely used for understanding basic biology, and have significantly contributed to the study of human disease. In recent years, genomic analysis has provided extensive evidence of widespread conservation of gene sequence and function amongst eukaryotes, allowing insights from model organisms to help decipher gene function in a wider range of species. The InterMOD consortium is developing an infrastructure based around the InterMine data warehouse system to integrate genomic and functional data from a number of key model organisms, leading the way to improved cross-species research. So far including budding yeast, nematode worm, fruit fly, zebrafish, rat and mouse, the project has set up data warehouses, synchronized data models, and created analysis tools and links between data from different species. The project unites a number of major model organism databases, improving both the consistency and accessibility of comparative research, to the benefit of the wider scientific community.
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Petri V, Jayaraman P, Tutaj M, Hayman GT, Smith JR, De Pons J, Laulederkind SJ, Lowry TF, Nigam R, Wang SJ, Shimoyama M, Dwinell MR, Munzenmaier DH, Worthey EA, Jacob HJ. The pathway ontology - updates and applications. J Biomed Semantics 2014; 5:7. [PMID: 24499703 PMCID: PMC3922094 DOI: 10.1186/2041-1480-5-7] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Accepted: 12/05/2013] [Indexed: 01/13/2023] Open
Abstract
Background The Pathway Ontology (PW) developed at the Rat Genome Database (RGD), covers all types of biological pathways, including altered and disease pathways and captures the relationships between them within the hierarchical structure of a directed acyclic graph. The ontology allows for the standardized annotation of rat, and of human and mouse genes to pathway terms. It also constitutes a vehicle for easy navigation between gene and ontology report pages, between reports and interactive pathway diagrams, between pathways directly connected within a diagram and between those that are globally related in pathway suites and suite networks. Surveys of the literature and the development of the Pathway and Disease Portals are important sources for the ongoing development of the ontology. User requests and mapping of pathways in other databases to terms in the ontology further contribute to increasing its content. Recently built automated pipelines use the mapped terms to make available the annotations generated by other groups. Results The two released pipelines – the Pathway Interaction Database (PID) Annotation Import Pipeline and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Annotation Import Pipeline, make available over 7,400 and 31,000 pathway gene annotations, respectively. Building the PID pipeline lead to the addition of new terms within the signaling node, also augmented by the release of the RGD “Immune and Inflammatory Disease Portal” at that time. Building the KEGG pipeline lead to a substantial increase in the number of disease pathway terms, such as those within the ‘infectious disease pathway’ parent term category. The ‘drug pathway’ node has also seen increases in the number of terms as well as a restructuring of the node. Literature surveys, disease portal deployments and user requests have contributed and continue to contribute additional new terms across the ontology. Since first presented, the content of PW has increased by over 75%. Conclusions Ongoing development of the Pathway Ontology and the implementation of pipelines promote an enriched provision of pathway data. The ontology is freely available for download and use from the RGD ftp site at ftp://rgd.mcw.edu/pub/ontology/pathway/ or from the National Center for Biomedical Ontology (NCBO) BioPortal website at http://bioportal.bioontology.org/ontologies/PW.
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Affiliation(s)
- Victoria Petri
- Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI, USA.
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Wu C, Schwartz JM, Nenadic G. PathNER: a tool for systematic identification of biological pathway mentions in the literature. BMC SYSTEMS BIOLOGY 2013; 7 Suppl 3:S2. [PMID: 24555844 PMCID: PMC3852116 DOI: 10.1186/1752-0509-7-s3-s2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Background Biological pathways are central to many biomedical studies and are frequently discussed in the literature. Several curated databases have been established to collate the knowledge of molecular processes constituting pathways. Yet, there has been little focus on enabling systematic detection of pathway mentions in the literature. Results We developed a tool, named PathNER (Pathway Named Entity Recognition), for the systematic identification of pathway mentions in the literature. PathNER is based on soft dictionary matching and rules, with the dictionary generated from public pathway databases. The rules utilise general pathway-specific keywords, syntactic information and gene/protein mentions. Detection results from both components are merged. On a gold-standard corpus, PathNER achieved an F1-score of 84%. To illustrate its potential, we applied PathNER on a collection of articles related to Alzheimer's disease to identify associated pathways, highlighting cases that can complement an existing manually curated knowledgebase. Conclusions In contrast to existing text-mining efforts that target the automatic reconstruction of pathway details from molecular interactions mentioned in the literature, PathNER focuses on identifying specific named pathway mentions. These mentions can be used to support large-scale curation and pathway-related systems biology applications, as demonstrated in the example of Alzheimer's disease. PathNER is implemented in Java and made freely available online at http://sourceforge.net/projects/pathner/.
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Wang SJ, Laulederkind SJF, Hayman GT, Smith JR, Petri V, Lowry TF, Nigam R, Dwinell MR, Worthey EA, Munzenmaier DH, Shimoyama M, Jacob HJ. Analysis of disease-associated objects at the Rat Genome Database. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat046. [PMID: 23794737 PMCID: PMC3689439 DOI: 10.1093/database/bat046] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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
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Affiliation(s)
- Shur-Jen Wang
- Rat Genome Database, Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA.
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Laulederkind SJF, Hayman GT, Wang SJ, Lowry TF, Nigam R, Petri V, Smith JR, Dwinell MR, Jacob HJ, Shimoyama M. Exploring genetic, genomic, and phenotypic data at the rat genome database. ACTA ACUST UNITED AC 2013; Chapter 1:1.14.1-1.14.27. [PMID: 23255149 DOI: 10.1002/0471250953.bi0114s40] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
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, http://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, and cellular components 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.
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12
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Laulederkind SJF, Hayman GT, Wang SJ, Smith JR, Lowry TF, Nigam R, Petri V, de Pons J, Dwinell MR, Shimoyama M, Munzenmaier DH, Worthey EA, Jacob HJ. The Rat Genome Database 2013--data, tools and users. Brief Bioinform 2013; 14:520-6. [PMID: 23434633 PMCID: PMC3713714 DOI: 10.1093/bib/bbt007] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
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.
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Affiliation(s)
- Stanley J F Laulederkind
- Human and Molecular Genetics Center, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI 53226-3548, USA.
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The updated RGD Pathway Portal utilizes increased curation efficiency and provides expanded pathway information. Hum Genomics 2013; 7:4. [PMID: 23379628 PMCID: PMC3598722 DOI: 10.1186/1479-7364-7-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2012] [Accepted: 12/10/2012] [Indexed: 01/27/2023] Open
Abstract
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.
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Laulederkind SJF, Tutaj M, Shimoyama M, Hayman GT, Lowry TF, Nigam R, Petri V, Smith JR, Wang SJ, de Pons J, Dwinell MR, Jacob HJ. Ontology searching and browsing at the Rat Genome Database. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2012; 2012:bas016. [PMID: 22434847 PMCID: PMC3308169 DOI: 10.1093/database/bas016] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
The Rat Genome Database (RGD) is the premier repository of rat genomic and genetic data and currently houses over 40 000 rat gene records, as well as human and mouse orthologs, 1857 rat and 1912 human quantitative trait loci (QTLs) and 2347 rat strains. Biological information curated for these data objects includes disease associations, phenotypes, pathways, molecular functions, biological processes and cellular components. RGD uses more than a dozen different ontologies to standardize annotation information for genes, QTLs and strains. That means a lot of time can be spent searching and browsing ontologies for the appropriate terms needed both for curating and mining the data. RGD has upgraded its ontology term search to make it more versatile and more robust. A term search result is connected to a term browser so the user can fine-tune the search by viewing parent and children terms. Most publicly available term browsers display a hierarchical organization of terms in an expandable tree format. RGD has replaced its old tree browser format with a ‘driller’ type of browser that allows quicker drilling up and down through the term branches, which has been confirmed by testing. The RGD ontology report pages have also been upgraded. Expanded functionality allows more choice in how annotations are displayed and what subsets of annotations are displayed. The new ontology search, browser and report features have been designed to enhance both manual data curation and manual data extraction. Database URL:http://rgd.mcw.edu/rgdweb/ontology/search.html
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Affiliation(s)
- Stanley J F Laulederkind
- Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI 53226-3548, USA.
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Parikh PP, Minning TA, Nguyen V, Lalithsena S, Asiaee AH, Sahoo SS, Doshi P, Tarleton R, Sheth AP. A semantic problem solving environment for integrative parasite research: identification of intervention targets for Trypanosoma cruzi. PLoS Negl Trop Dis 2012; 6:e1458. [PMID: 22272365 PMCID: PMC3260319 DOI: 10.1371/journal.pntd.0001458] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2011] [Accepted: 11/18/2011] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Research on the biology of parasites requires a sophisticated and integrated computational platform to query and analyze large volumes of data, representing both unpublished (internal) and public (external) data sources. Effective analysis of an integrated data resource using knowledge discovery tools would significantly aid biologists in conducting their research, for example, through identifying various intervention targets in parasites and in deciding the future direction of ongoing as well as planned projects. A key challenge in achieving this objective is the heterogeneity between the internal lab data, usually stored as flat files, Excel spreadsheets or custom-built databases, and the external databases. Reconciling the different forms of heterogeneity and effectively integrating data from disparate sources is a nontrivial task for biologists and requires a dedicated informatics infrastructure. Thus, we developed an integrated environment using Semantic Web technologies that may provide biologists the tools for managing and analyzing their data, without the need for acquiring in-depth computer science knowledge. METHODOLOGY/PRINCIPAL FINDINGS We developed a semantic problem-solving environment (SPSE) that uses ontologies to integrate internal lab data with external resources in a Parasite Knowledge Base (PKB), which has the ability to query across these resources in a unified manner. The SPSE includes Web Ontology Language (OWL)-based ontologies, experimental data with its provenance information represented using the Resource Description Format (RDF), and a visual querying tool, Cuebee, that features integrated use of Web services. We demonstrate the use and benefit of SPSE using example queries for identifying gene knockout targets of Trypanosoma cruzi for vaccine development. Answers to these queries involve looking up multiple sources of data, linking them together and presenting the results. CONCLUSION/SIGNIFICANCE The SPSE facilitates parasitologists in leveraging the growing, but disparate, parasite data resources by offering an integrative platform that utilizes Semantic Web techniques, while keeping their workload increase minimal.
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Affiliation(s)
- Priti P. Parikh
- The Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, United States of America
| | - Todd A. Minning
- Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Vinh Nguyen
- The Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, United States of America
| | - Sarasi Lalithsena
- The Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, United States of America
| | - Amir H. Asiaee
- THINC Lab, Department of Computer Science, University of Georgia, Athens, Georgia, United States of America
| | - Satya S. Sahoo
- The Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, United States of America
- Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Prashant Doshi
- THINC Lab, Department of Computer Science, University of Georgia, Athens, Georgia, United States of America
| | - Rick Tarleton
- Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Amit P. Sheth
- The Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, United States of America
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Hammond S, Kaplarevic M, Borth N, Betenbaugh MJ, Lee KH. Chinese hamster genome database: an online resource for the CHO community at www.CHOgenome.org. Biotechnol Bioeng 2011; 109:1353-6. [PMID: 22105744 DOI: 10.1002/bit.24374] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2011] [Accepted: 10/31/2011] [Indexed: 01/26/2023]
Abstract
The Chinese hamster genome database (http://www.chogenome.org/) is an online resource for the Chinese hamster (Cricetulus griseus) and Chinese hamster ovary (CHO) cell communities. CHO cells are important for biomedical research and are widely used in industry for the production of biopharmaceuticals. The genome of the CHO-K1 cell line was recently sequenced and the CHO community has developed an online resource to facilitate accessibility of the genomic data and the development of genomic tools.
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Affiliation(s)
- Stephanie Hammond
- Department of Chemical Engineering, University of Delaware, Newark, Delaware, USA
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