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http://function.princeton.edu/GOLEM/index.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 7, 2017. Welcome to the home of GOLEM: An interactive, graphical gene-ontology visualization, navigation,and analysis tool on the web. GOLEM is a useful tool which allows the viewer to navigate and explore a local portion of the Gene Ontology (GO) hierarchy. Users can also load annotations for various organisms into the ontology in order to search for particular genes, or to limit the display to show only GO terms relevant to a particular organism, or to quickly search for GO terms enriched in a set of query genes. GOLEM is implemented in Java, and is available both for use on the web as an applet, and for download as a JAR package. A brief tutorial on how to use GOLEM is available both online and in the instructions included in the program. We also have a list of links to libraries used to make GOLEM, as well as the various organizations that curate organism annotations to the ontology. GOLEM is available as a .jar package and a macintosh .app for use on- or off- line as a stand-alone package. You will need to have Java (v.1.5 or greater) installed on your system to run GOLEM. Source code (including Eclipse project files) are also available. GOLEM (Gene Ontology Local Exploration Map)is a visualization and analysis tool for focused exploration of the gene ontology graph. GOLEM allows the user to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. It also supports rapid analysis of an input list of genes to find enriched gene ontology terms. The GOLEM application permits the user either to utilize local gene ontology and annotations files in the absence of an Internet connection, or to access the most recent ontology and annotation information from the gene ontology webpage. GOLEM supports global and organism-specific searches by gene ontology term name, gene ontology id and gene name. CONCLUSION: GOLEM is a useful software tool for biologists interested in visualizing the local directed acyclic graph structure of the gene ontology hierarchy and searching for gene ontology terms enriched in genes of interest. It is freely available both as an application and as an applet.

Proper citation: GOLEM An interactive, graphical gene-ontology visualization, navigation, and analysis tool (RRID:SCR_003191) Copy   

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http://www.emdataresource.org/

Portal for deposition and retrieval of cryo electron microscopy (3DEM) density maps, atomic models, and associated metadata. Global resource for 3 Dimensional Electron Microscopy structure data archiving and retrieval, news, events, software tools, data standards, validation methods.

Proper citation: EMDataResource.org (RRID:SCR_003207) Copy   

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http://dip.doe-mbi.ucla.edu/

Database to catalog experimentally determined interactions between proteins combining information from a variety of sources to create a single, consistent set of protein-protein interactions that can be downloaded in a variety of formats. The data were curated, both, manually and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks. Tools are available to analyze, visualize and integrate user's own experimental data with the information about protein-protein interactions available in the DIP database. The DIP database lists protein pairs that are known to interact with each other. By interact they mean that two amino acid chains were experimentally identified to bind to each other. The database lists such pairs to aid those studying a particular protein-protein interaction but also those investigating entire regulatory and signaling pathways as well as those studying the organization and complexity of the protein interaction network at the cellular level. Registration is required to gain access to most of the DIP features. Registration is free to the members of the academic community. Trial accounts for the commercial users are also available.

Proper citation: Database of Interacting Proteins (DIP) (RRID:SCR_003167) Copy   

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http://www.broadinstitute.org/cancer/software/genepattern

A powerful genomic analysis platform that provides access to hundreds of tools for gene expression analysis, proteomics, SNP analysis, flow cytometry, RNA-seq analysis, and common data processing tasks. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research.

Proper citation: GenePattern (RRID:SCR_003201) Copy   

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http://umbbd.msi.umn.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 27, 2014. Database containing information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. Its goal is to provide information on microbial enzyme-catalyzed reactions that are important for biotechnology. The reactions covered are studied for basic understanding of nature, biocatalysis leading to specialty chemical manufacture, and biodegradation of environmental pollutants. Individual reactions and metabolic pathways are presented with information on the starting and intermediate chemical compounds, the organisms that transform the compounds, the enzymes, and the genes. The present database has been successfully used to teach enzymology and use of biochemical Internet information resources to advanced undergraduate and graduate students, and is being expanded primarily with the help of such students. In addition to reactions and pathways, this database also contains Biochemical Periodic Tables and a Pathway Prediction System. * Search the UM-BBD for compound, enzyme, microorganism, pathway, or BT rule name; chemical formula; chemical structure; CAS Registry Number; or EC code. * Go to Pathways and Metapathways in the UM-BBD * Lists of 203 pathways; 1400 reactions; 1296 compounds; 916 enzymes; 510 microorganism entries; 245 biotransformation rules; 50 organic functional groups; 76 reactions of naphthalene 1,2-dioxygenase; 109 reactions of toluene dioxygenase; Graphical UM-BBD Overview; and Other Graphics (Metapathway and Pathway Maps and Reaction Mechanisms).

Proper citation: UM-BBD (RRID:SCR_005787) Copy   

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Ratings or validation data are available for this resource

http://genome.ucsc.edu/

Portal to interactively visualize genomic data. Provides reference sequences and working draft assemblies for collection of genomes and access to ENCODE and Neanderthal projects. Includes collection of vertebrate and model organism assemblies and annotations, along with suite of tools for viewing, analyzing and downloading data.

Proper citation: UCSC Genome Browser (RRID:SCR_005780) Copy   

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http://stormo.wustl.edu/ScerTF

Catalog of over 1,200 position weight matrices (PWMs) for 196 different yeast transcription factors (TFs). They've curated 11 literature sources, benchmarked the published position-specific scoring matrices against in-vivo TF occupancy data and TF deletion experiments, and combined the most accurate models to produce a single collection of the best performing weight matrices for Saccharomyces cerevisiae. ScerTF is useful for a wide range of problems, such as linking regulatory sites with transcription factors, identifying a transcription factor based on a user-input matrix, finding the genes bound/regulated by a particular TF, and finding regulatory interactions between transcription factors. Enter a TF name to find the recommended matrix for a particular TF, or enter a nucleotide sequence to identify all TFs that could bind a particular region.

Proper citation: ScerTF (RRID:SCR_006121) Copy   

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http://evolution.genetics.washington.edu/phylip.html

A free package of software programs for inferring phylogenies (evolutionary trees). The source code is distributed (in C), and executables are also distributed. In particular, already-compiled executables are available for Windows (95/98/NT/2000/me/xp/Vista), Mac OS X, and Linux systems. Older executables are also available for Mac OS 8 or 9 systems.

Proper citation: PHYLIP (RRID:SCR_006244) Copy   

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http://dictybase.org/

Model organism database for the social amoeba Dictyostelium discoideum that provides the biomedical research community with integrated, high quality data and tools for Dictyostelium discoideum and related species. dictyBase houses the complete genome sequence, ESTs, and the entire body of literature relevant to Dictyostelium. This information is curated to provide accurate gene models and functional annotations, with the goal of fully annotating the genome to provide a ''''reference genome'''' in the Amoebozoa clade. They highlight several new features in the present update: (i) new annotations; (ii) improved interface with web 2.0 functionality; (iii) the initial steps towards a genome portal for the Amoebozoa; (iv) ortholog display; and (v) the complete integration of the Dicty Stock Center with dictyBase. The Dicty Stock Center currently holds over 1500 strains targeting over 930 different genes. There are over 100 different distinct amoebozoan species. In addition, the collection contains nearly 600 plasmids and other materials such as antibodies and cDNA libraries. The strain collection includes: * strain catalog * natural isolates * MNNG chemical mutants * tester strains for parasexual genetics * auxotroph strains * null mutants * GFP-labeled strains for cell biology * plasmid catalog The Dicty Stock Center can accept Dictyostelium strains, plasmids, and other materials relevant for research using Dictyostelium such as antibodies and cDNA or genomic libraries.

Proper citation: Dictyostelium discoideum genome database (RRID:SCR_006643) Copy   

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http://nramm.scripps.edu/

Biomedical technology research center that develops, tests and applies technology aimed toward completely automating the processes involved in solving macromolecular structures using cryo-electron microscopy. The goal is to establish a resource that will serve both as a center for high-throughput molecular microscopy as well as for transferring this technique to the research community. Current Core Technology Research and Development is focused on 4 areas: improving grid substrates and specimen preparation; further automation and optimization of image acquisition; development of an integrated single particle analysis and processing pipeline; and the development of automated high throughput EM screening. NRAMM welcomes applications of both collaborative and service projects.

Proper citation: National Resource for Automated Molecular Microscopy (RRID:SCR_001448) Copy   

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http://www.acert.cornell.edu/

Biomedical technology research center that develops methods, both experimental and theoretical, of modern electron spin resonance (ESR) for biomedical applications. Center technologies are applicable to the determination of the structure and complex dynamics of proteins. Principal areas of expertise: * Pulsed Fourier Transform and Two Dimensional ESR * High Frequency-High Field (HFHF) ESR * High Resolution ESR Microscopy * Theory and Computational Methods for Modern ESR Activities include: * making resources available to the biomedical community, * publishing results, * running workshops on the new methodologies, * addressing the need to bring these new technologies to other laboratories.

Proper citation: National Biomedical Center for Advanced ESR Technology (RRID:SCR_001444) Copy   

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http://www.sci.utah.edu/cibc/

Biomedical technology research center that produces open-source software tools for biomedical image-based modeling, biomedical simulation and estimation, and the visualization of biomedical data. The Center works closely with software users and collaborators in a range of scientific domains to produce user-optimized tools and provides advice, technical support, workshops, and education to enhance user success. Biological projects and collaborations drive their development efforts, all with a single unifying vision: to develop the role of image-based modeling and analysis in biomedical science and clinical practice. The CIBC has a strong, ongoing emphasis on software simulation of bioelectric fields, with clinically oriented collaborations in cardiac defibrillation and the diagnosis/treatment of epilepsy. In addition, the CIBC has expanded in recent years to include applications of statistical shape analysis and three-dimensional visualization to mouse genetics and neuroimaging and applications of image and geometry processing to cell biology.

Proper citation: Center for Integrative Biomedical Computing (RRID:SCR_001961) Copy   

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http://www.ks.uiuc.edu/

Biomedical technology research center focusing on the structure and function of supramolecular systems in the living cell as well as on the development of new algorithms and efficient computing tools for physical biology. They bring the most advanced molecular modeling, bioinformatics, and computational technologies to bear on questions of biomedical relevance. They extend, refine and deliver these technologies in response to experimental progress and emerging needs of the wide biomedical research community. They magnify the impact of their work through direct collaboration with experimental researchers, the distribution of cutting-edge and user-friendly software, and via extensive training, service, and dissemination efforts. The multidisciplinary team is engaged in the modeling of large macromolecular systems in realistic environments, and has produced ground-breaking insights into biomolecular processes coupled with mechanical force, bioelectronic processes in metabolism and vision, and with the function and mechanism of membrane proteins. They are committed and work towards further advancement of * Molecular modeling tools which can integrate structural information with bioinformatics databases and molecular dynamics simulations, and which can be used by a wide audience; * High performance molecular visualization and simulation software, capable of modeling biomolecules in realistic environments of 100,000,000 atoms or more; * Conceptual and methodological foundations of molecular modeling in the fields of quantum biology, mechanobiology, and interactive modeling; * Biomedical science through collaborations between theoretical and experimental researchers; * Support of the entire research process and training through a web-enabled collaborative environment; and * Service, training, and dissemination by leveraging web-based molecular graphics and integrated modeling technologies.

Proper citation: NIH Center for Macromolecular Modeling and Bioinformatics (RRID:SCR_001435) Copy   

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http://nrnb.org/index.html

Biomedical technology research center that develops new algorithms, visualizations and conceptual frameworks to study biological networks at multiple levels and scales, from protein-protein and genetic interactions to cell-cell communication and vast social networks. They are developing freely available, open-source suite of software technology that broadly enables network-based visualization, analysis, and biomedical discovery for NIH-funded researchers. This software is enabling researchers to assemble large-scale biological data into models of networks and pathways and to use these networks to better understand how biological systems operate under normal conditions and how they fail in disease. The National Resource for Network Biology is organized around the following key components: Technology Research and Development, Driving Biomedical Projects, Outreach, Training and Dissemination of Tools. The NRNB supports several types of training events, including both virtual and live workshops; tutorials sessions for clinicians, biologists and bioinformaticians; presentations and demonstrations at conferences; online tutorials and webcasts; and annual symposium.

Proper citation: National Resource for Network Biology (RRID:SCR_004259) Copy   

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http://depts.washington.edu/yeastrc/

Biomedical technology research center that (1) exploits the budding yeast Saccharomyces cerevisiae to develop novel technologies for investigating and characterizing protein function and protein structure (2) facilitates research and extension of new technologies through collaboration, and (3) actively disseminates data and technology to the research community. Through collaboration, the YRC freely provides resources and expertise in six core technology areas: Protein Tandem Mass Spectrometry, Protein Sequence-Function Relationships, Quantitative Phenotyping, Protein Structure Prediction and Design, Fluorescence Microscopy, Computational Biology.

Proper citation: Yeast Resource Center (RRID:SCR_007942) Copy   

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http://snyderome.stanford.edu/

Data set generated by personal omics profiling of Dr. Michael Snyder at Stanford University. It combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. The analysis revealed various medical risks, including type II diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions.

Proper citation: iPOP (RRID:SCR_008991) Copy   

•   Source: SciCrunch Registry

http://proteomics.ucsd.edu/

Biomedical technology research center that focuses on the computational bottlenecks that impair the interpretation of data, bringing modern algorithmic approaches to mass spectrometry and building a new generation of reliable, open-access software tools to support both new mass spectrometry instrumentation and emerging applications.

Proper citation: Center for Computational Mass Spectrometry (RRID:SCR_008161) Copy   

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http://msr.dom.wustl.edu/

Biomedical technology research center that develops mass spectrometry-based tools for the study of proteins, lipids and metaboilites. These include biomarker identification, stable isotope mass spectrometry and the analysis of intact proteins. Our goals are: * to conduct basic research in the science of mass spectrometry * to establish collaborative research projects with scientists at WU and at other institutions * to provide a service in mass spectrometry * to educate and train students in mass spectrometry * to disseminate results of our research and descriptions of the subject of mass spectrometry

Proper citation: NIH / NCRR Mass Spectrometry Resource Washington University in St. Louis (RRID:SCR_009009) Copy   

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http://glycotech.ccrc.uga.edu/

Biomedical technology research center that develops technologies to increase understanding of the molecular basis of the involvement of carbohydrates in protein-carbohydrate interactions in disease and to develop more powerful technologies necessary to achieve this goal. Complex carbohydrates play an important role in many biomedically important processes, including inflammatory response, hormone action, malignancy, viral and bacterial infections and cell differentiation. The resource combines complimentary technologies: synthetic chemistry, nuclear magnetic resonance, mass spectrometry, computational biology, protein expression and cell-based assays. As new technologies are developed, application to these processes will be pursued through collaborative and service projects.

Proper citation: Resource for Integrated Glycotechnology (RRID:SCR_009008) Copy   

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http://cell.ccrc.uga.edu/world/glycomics/glycomics.php

Biomedical technology research center that develops and implements new technologies to investigate the glycome of cells, including glycoproteomics and glycoconjugate analysis, transcript analysis and bioinformatics. It develops the tools and technology to analyze in detail the glycoprotein and glycolipid expression of mouse embryonic stem cells and the cells into which they differentiate. The technology developed in the Center will allow an understanding of how glycosylation is controlled during differentiation and will allow the development of tools to promote the use of stem cells to treat human disease. In addition, the technology developed will be applicable to the study of other cell types, including cancer cells that are progressing to a more invasive phenotype. The technology developed will also allow others in the scientific community to participate in glycomics research through dissemination of the new methods developed and through the analytical services provided by the resource to other scientists requesting assistance in glycomic analyses.

Proper citation: Integrated Technology Resource for Biomedical Glycomics (RRID:SCR_009003) Copy   

•   Source: SciCrunch Registry