Mouse Genome Database (MGD)-2018: Knowledgebase for the Laboratory Mouse Cynthia L
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Renoprotective Effect of Combined Inhibition of Angiotensin-Converting Enzyme and Histone Deacetylase
BASIC RESEARCH www.jasn.org Renoprotective Effect of Combined Inhibition of Angiotensin-Converting Enzyme and Histone Deacetylase † ‡ Yifei Zhong,* Edward Y. Chen, § Ruijie Liu,*¶ Peter Y. Chuang,* Sandeep K. Mallipattu,* ‡ ‡ † | ‡ Christopher M. Tan, § Neil R. Clark, § Yueyi Deng, Paul E. Klotman, Avi Ma’ayan, § and ‡ John Cijiang He* ¶ *Department of Medicine, Mount Sinai School of Medicine, New York, New York; †Department of Nephrology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; ‡Department of Pharmacology and Systems Therapeutics and §Systems Biology Center New York, Mount Sinai School of Medicine, New York, New York; |Baylor College of Medicine, Houston, Texas; and ¶Renal Section, James J. Peters Veterans Affairs Medical Center, New York, New York ABSTRACT The Connectivity Map database contains microarray signatures of gene expression derived from approximately 6000 experiments that examined the effects of approximately 1300 single drugs on several human cancer cell lines. We used these data to prioritize pairs of drugs expected to reverse the changes in gene expression observed in the kidneys of a mouse model of HIV-associated nephropathy (Tg26 mice). We predicted that the combination of an angiotensin-converting enzyme (ACE) inhibitor and a histone deacetylase inhibitor would maximally reverse the disease-associated expression of genes in the kidneys of these mice. Testing the combination of these inhibitors in Tg26 mice revealed an additive renoprotective effect, as suggested by reduction of proteinuria, improvement of renal function, and attenuation of kidney injury. Furthermore, we observed the predicted treatment-associated changes in the expression of selected genes and pathway components. In summary, these data suggest that the combination of an ACE inhibitor and a histone deacetylase inhibitor could have therapeutic potential for various kidney diseases. -
Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse Janan T
ILAR Journal, 2017, Vol. 58, No. 1, 17–41 doi: 10.1093/ilar/ilx013 Article Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse Janan T. Eppig Janan T. Eppig, PhD, is Professor Emeritus at The Jackson Laboratory in Bar Harbor, Maine. Address correspondence to Dr. Janan T. Eppig, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609 or email [email protected] Abstract The Mouse Genome Informatics (MGI) Resource supports basic, translational, and computational research by providing high-quality, integrated data on the genetics, genomics, and biology of the laboratory mouse. MGI serves a strategic role for the scientific community in facilitating biomedical, experimental, and computational studies investigating the genetics and processes of diseases and enabling the development and testing of new disease models and therapeutic interventions. This review describes the nexus of the body of growing genetic and biological data and the advances in computer technology in the late 1980s, including the World Wide Web, that together launched the beginnings of MGI. MGI develops and maintains a gold-standard resource that reflects the current state of knowledge, provides semantic and contextual data integration that fosters hypothesis testing, continually develops new and improved tools for searching and analysis, and partners with the scientific community to assure research data needs are met. Here we describe one slice of MGI relating to the development of community-wide large-scale mutagenesis and phenotyping projects and introduce ways to access and use these MGI data. References and links to additional MGI aspects are provided. Key words: database; genetics; genomics; human disease model; informatics; model organism; mouse; phenotypes Introduction strains and special purpose strains that have been developed The laboratory mouse is an essential model for understanding provide fertile ground for population studies and the potential human biology, health, and disease. -
What's New in Flybase
What’s new in FlyBase (in its 25th year) Steven Marygold • JBrowse • GAL4 search • Gene Snapshots • New weBsite: FlyBase 2.0 • Gene Groups • Gene2Function • Orthologs • Alliance of Genome Resources • Human Disease Models Outline 1. Recent additions to FlyBase 2. New features in FlyBase 2.0 3. Multi-species dataBases 4. Further information/feedBack Outline 1. Recent additions to FlyBase 2. New features in FlyBase 2.0 3. Multi-species dataBases 4. Further information/feedBack JBrowse Gene Snapshots Epidermal growth factor receptor (Egfr) is the transmembrane tyrosine kinase receptor for signaling ligands in the TGFα family (grk, spi, vn, and Krn), which utilises the intracellular MAP kinase pathway. Egfr roles include growth regulation, cell survival and developmental patterning. Gene Groups • gene products sharing molecular function (kinases, tRNAs…) • gene families (actins, odorant receptors…) • suBunits of complexes (riBosome, spliceosome…) Gene Group Reports Manually written description of group, with notes List of members, with export to: 1. Hit-list 2. Batch Download 3. Orthologs tool Links to external resources Orthology search tool Orthology search tool Orthology search results Human Disease Model data Human Disease Model Reports Manually written summary of disease and model Links to related diseases/fly models List of fly stocks used in model ‘GAL4 etc.’ search tool ‘GAL4 etc.’ search results Community Interactions Outline 1. Recent additions to FlyBase 2. New features in FlyBase 2.0 3. Multi-species dataBases 4. Further information/feedBack New features include: • New/Improved tools – New hit-list management – Sequence Downloader – Revised Jump-to-Gene/Search Box • Enhanced Report pages – Navigation panel – GO summary riBBons (Gene Report) – Protein domain graphics (Gene/Polypeptide Report) – Reference filtering • Mobile device friendly New hit-lists New hit-lists New hit-lists New hit-lists New hit-lists New hit-lists Revised Gene Reports Revised Gene Reports Revised Gene Reports Revised Gene Reports Revised Jump to Gene/Search Outline 1. -
Introduction to Genome Informatics
GenomeGenome InformaticsInformatics Systems Biology and the Omics Cascade (Course 2143) Day 3, June 11th, 2008 Kiyoko F. Aoki-Kinoshita IntroductionIntroduction GenomeGenome informaticsinformatics coverscovers thethe computercomputer-- basedbased modelingmodeling andand datadata processingprocessing ofof genomegenome--basedbased data.data. ThisThis includesincludes databasesdatabases andand resourcesresources forfor genomicgenomic analysis.analysis. YouYou werewere introducedintroduced toto KEGGKEGG onon DayDay 2.2. SomeSome otherother usefuluseful databasesdatabases andand resourcesresources willwill bebe coveredcovered today.today. ButBut first!first! DataData formatsformats ItIt isis usuallyusually notnot enoughenough toto simplysimply looklook atat thethe datadata providedprovided byby databasesdatabases ToTo actuallyactually useuse thethe datadata forfor analysis,analysis, oneone oftenoften needsneeds toto savesave thethe retrievedretrieved datadata ThisThis requiresrequires knowledgeknowledge aboutabout thethe datadata formatsformats usedused byby eacheach databasedatabase SoSo wewe willwill covercover thethe majormajor datadata formatsformats usedused inin bioinformaticsbioinformatics DataData FormatsFormats MajorMajor datadata formats:formats: –– GenBankGenBank –– EMBLEMBL/UniProt/UniProt –– FASTAFASTA –– PDBPDB FormatsFormats suitedsuited forfor programming:programming: –– ASN.1ASN.1 (Abstract(Abstract SyntaxSyntax NotationNotation One)One) –– XMLXML (eXtensible(eXtensible MarkupMarkup Language)Language) GenBankGenBank formatformat -
Tagging and Identification of Animal Resources Workshop Report September 6, 2017
Tagging and Identification of Animal Resources Workshop Division of Comparative Medicine Office of Research Infrastructure Programs Division of Program Coordination, Planning, and Strategic Initiatives Office of the Director National Institutes of Health Wednesday, September 6, 2017 Bethesda, MD Workshop Report Tagging and Identification of Animal Resources Workshop Report September 6, 2017 Table of Contents A. Overview ..............................................................................................................1 B. Introduction and Welcome ................................................................................1 C. Summary of Presentations and Discussions ..................................................... 2 Rigor and Reproducibility in Biomedical Research: Current NIH Guidelines ....................... 2 Session 1: Identification of Animal Resources in Scientific Literature and Grant Reports .... 3 Session 2: Animal Repositories and Their Role in Supporting High-Standard Research ....... 5 Session 3: Common Publications Guidelines for Citing Animal Resources ........................... 7 Session 4: Future Development of Animal Resource Identifiers ............................................ 8 Panel Discussion: Animal Resources and the Challenges of Unique Identifiers .................. 10 Session 5: Hands-on Session with RRIDs ............................................................................. 11 D. Closing Remarks and Recommendations ....................................................... 12 Appendix -
Creating the Gene Ontology Resource: Design and Implementation
Downloaded from genome.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press Resource Creating the Gene Ontology Resource: Design and Implementation The Gene Ontology Consortium2 The exponential growth in the volume of accessible biological information has generated a confusion of voices surrounding the annotation of molecular information about genes and their products. The Gene Ontology (GO) project seeks to provide a set of structured vocabularies for specific biological domains that can be used to describe gene products in any organism. This work includes building three extensive ontologies to describe molecular function, biological process, and cellular component, and providing a community database resource that supports the use of these ontologies. The GO Consortium was initiated by scientists associated with three model organism databases: SGD, the Saccharomyces Genome database; FlyBase, the Drosophila genome database; and MGD/GXD, the Mouse Genome Informatics databases. Additional model organism database groups are joining the project. Each of these model organism information systems is annotating genes and gene products using GO vocabulary terms and incorporating these annotations into their respective model organism databases. Each database contributes its annotation files to a shared GO data resource accessible to the public at http://www.geneontology.org/. The GO site can be used by the community both to recover the GO vocabularies and to access the annotated gene product data sets from the model organism databases. The GO Consortium supports the development of the GO database resource and provides tools enabling curators and researchers to query and manipulate the vocabularies. We believe that the shared development of this molecular annotation resource will contribute to the unification of biological information. -
Using Wormbase, a Genome Biology Resource for Caenorhabditis Elegans and Related Nematodes
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Methods Manuscript Author Mol Biol. Author Manuscript Author manuscript; available in PMC 2019 March 20. Published in final edited form as: Methods Mol Biol. 2018 ; 1757: 399–470. doi:10.1007/978-1-4939-7737-6_14. Using WormBase, a genome biology resource for Caenorhabditis elegans and related nematodes Christian Grove1, Scott Cain2, Wen J. Chen1, Paul Davis3, Todd Harris2, Kevin Howe3, Ranjana Kishore1, Raymond Lee1, Michael Paulini3, Daniela Raciti1, Mary Ann Tuli1, Kimberly Van Auken1, Gary Williams3, and The WormBase Consortium* 1Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA 2Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada 3European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK Abstract WormBase (www.wormbase.org) provides the nematode research community with a centralized database for information pertaining to nematode genes and genomes. As more nematode genome sequences are becoming available and as richer data sets are published, WormBase strives to maintain updated information, displays, and services to facilitate efficient access to and understanding of the knowledge generated by the published nematode genetics literature. This chapter aims to provide an explanation of how to use basic features of WormBase, new features, and some commonly used tools and data queries. Explanations of the curated data and step-by-step instructions of how to access the data via the WormBase website and available data mining tools are provided. Keywords data mining; nematodes; genomics; genetics; Caenorhabditis elegans; model organism database; ontologies; user guide 1 Introduction Since its inception in March 2000, WormBase has provided the nematode research community with an online resource for gene, genome, and other biological information about Caenorhabditis elegans and related nematodes [1,2]. -
Contents Nucleotide Sequence Databases
Contents Nucleotide Sequence Databases ......................................................................................................... 1 Protein Sequence Databases ............................................................................................................... 1 Database Searching by Sequence Similarity........................................................................................ 2 Human Genome Databases ................................................................................................................. 2 Databases of other Organisms ............................................................................................................. 3 Genome-wide Analysis ......................................................................................................................... 3 Protein Domains: Databases and Search Tools ................................................................................... 3 Motif and Pattern Search in Sequences .............................................................................................. 3 Protein 3D Structure............................................................................................................................. 4 Phylogeny & Taxonomy ....................................................................................................................... 4 Gene Prediction .................................................................................................................................... 4 Gene Expression