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http://ophid.utoronto.ca/i2d

Database of known and predicted mammalian and eukaryotic protein-protein interactions, it is designed to be both a resource for the laboratory scientist to explore known and predicted protein-protein interactions, and to facilitate bioinformatics initiatives exploring protein interaction networks. It has been built by mapping high-throughput (HTP) data between species. Thus, until experimentally verified, these interactions should be considered predictions. It remains one of the most comprehensive sources of known and predicted eukaryotic PPI. It contains 490,600 Source Interactions, 370,002 Predicted Interactions, for a total of 846,116 interactions, and continues to expand as new protein-protein interaction data becomes available.

Proper citation: I2D (RRID:SCR_002957) Copy   

•   Source: SciCrunch Registry

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://mouseNET.princeton.edu

A functional network for laboratory mouse based on integration of diverse genetic and genomic data. It allows the users to accurately predict novel functional assignments and network components. MouseNET uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the mouseNET algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. The graph may be explored further. As you move the mouse over genes in the network, interactions involving these genes are highlighted.If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed.

Proper citation: MouseNET (RRID:SCR_003357) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://rostlab.org/services/nlsdb/

A database of nuclear localization signals (NLSs) and of nuclear proteins targeted to the nucleus by NLS motifs. NLSs are short stretches of residues mediating transport of nuclear proteins into the nucleus. The database contains 114 experimentally determined NLSs that were obtained through an extensive literature search. Using "in silico mutagenesis" this set was extended to 308 experimental and potential NLSs. This final set matched over 43% of all known nuclear proteins and matches no currently known non-nuclear protein. NLSdb contains over 6000 predicted nuclear proteins and their targeting signals from the PDB and SWISS-PROT/TrEMBL databases. The database also contains over 12 500 predicted nuclear proteins from six entirely sequenced eukaryotic proteomes (Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana and Saccharomyces cerevisiae). NLS motifs often co-localize with DNA-binding regions. This observation was used to also annotate over 1500 DNA-binding proteins. From this site you can: * Query NLSdb * Find out how to use NLSdb * Browse the entries in NLSdb * Find out if your protein has an NLS using PredictNLS * Predict subcellular localization of your protein using LOCtree

Proper citation: NLSdb: a database of nuclear localization signals (RRID:SCR_003273) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.cellimagelibrary.org/

Freely accessible, public repository of vetted and annotated microscopic images, videos, and animations of cells from a variety of organisms, showcasing cell architecture, intracellular functionalities, and both normal and abnormal processes. Explore by Cell Process, Cell Component, Cell Type or Organism. The Cell includes images acquired from historical and modern collections, publications, and by recruitment.

Proper citation: Cell Image Library (CIL) (RRID:SCR_003510) Copy   

•   Source: SciCrunch Registry

http://www.reactome.org

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

Proper citation: Reactome (RRID:SCR_003485) Copy   

•   Source: SciCrunch Registry

http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

•   Source: SciCrunch Registry

http://bnaber.org

bNAber is the Broadly Neutralizing Antibody E-Resource Database, analysis, visualization, and data discovery tool for broadly neutralizing HIV-1 antibodies (bNAbs). bNAber seeks to be a vital tool in the search for an AIDS vaccine.

Proper citation: bNAber (RRID:SCR_010510) Copy   

•   Source: SciCrunch Registry

https://web.uri.edu/riinbre/mic/

Core provides sequencing and bioinformatics support for INBRE and non-INBRE researchers. Provides data science services adjacent to traditional bioinformatics; access to computational and software resources for INBRE network institutions, particularly primarily undergraduate institutions; training for students and faculty in data science methods. Maintains professional network with other core and user facilities in Rhode Island and beyond to maximize resources available to our users.Utilizes novel technologies such as virtual/augmented reality for use in teaching and research.

Proper citation: Rhode Island INBRE Molecular Informatics Core Facility (RRID:SCR_017685) Copy   

•   Source: SciCrunch Registry

https://core.bioinformatics.udel.edu

CBCB Bioinformatics Data Science Core builds upon infrastructure of the Center for Bioinformatics and Computational Biology. Delaware Biotechnology Institute , Delaware Data Science Institute, and Protein Information Resource. Core helps with planning your research or analyzing your current data. Services:Consultation and Collaboration, Proposal Assistance,Training, Bioinformatics Software, Computational Hardware, Database and Web Services, Next Gen Analysis, Custom Analysis.

Proper citation: University of Delaware CBCB Bioinformatics Data Science Core Facility (RRID:SCR_017696) Copy   

•   Source: SciCrunch Registry

https://med.und.edu/imaging/index.html

Provides advanced instrumentation for researchers interested in investigating biological processes at cellular, sub cellular and molecular level.Instrumentation include Confocal Microscope Systems :Zeiss LSM-510 Meta Confocal Microscope, Olympus FV1000 MPE Basic Multiphoton Microscope;Brightfield/Fluorescence: Nikon Eclipse TE300 (inverted) Fluorescent Microscope, Nikon Eclipse 80i (upright) Fluorescent Microscope, Leica Thunder Imager; TIRF Systems:Olympus TIRF Microscope; Transmission Electron Microscopy:Hitachi 7500 TEM, RCM MTX Ultramicrotomes;Scanning Electron Microscopy:Hitachi 4700 Field Emission SEM, Denton Desk II Sputter and Turbo-sputter Coaters.Light and electron microscopic facilities, complete with ancillary support equipment, are located in close proximity to each other within suite of rooms housed in medical school.

Proper citation: University of North Dakota School of Medicine and Health Sciences Imaging Core Facility (RRID:SCR_017733) Copy   

•   Source: SciCrunch Registry

http://www.uwyo.edu/microscopy/

Core to assist researchers and students in their imaging needs of fluorescence and electron microscopy and to increase use of microscopes in science education. Consultation, training or touring facility is also available by appointment.

Proper citation: Wyoming University Jenkins Microscopy Core Facility (RRID:SCR_017758) Copy   

•   Source: SciCrunch Registry

https://www.unr.edu/proteomics

Core offers mass spectral proteomic analysis. Assists with qualitative and quantitative characterization of proteins in biological matrices such as plasma/serum, tissue, cell lines and other biological material to gain understanding of physiological pathways, molecular interactions and regulatory signaling.

Proper citation: University of Nevada at Reno Nevada Proteomics Center Core Facility (RRID:SCR_017761) Copy   

•   Source: SciCrunch Registry

http://psf.cobre.ku.edu/cores/ppg/about

Core focuses on cloning, expression and purification of prokaryotic and eukaryotic proteins for COBRE and other investigators in Kansas and region. Laboratory maintains equipment to support production of properly folded proteins in quantities suitable for structural studies (X-ray and NMR), functional studies (catalytic or biological), label-free binding studies (SPR) and/or high throughput (HTP) screening studies.

Proper citation: Kansas University at Lawrence Protein Production Group Core Facility (RRID:SCR_017749) Copy   

•   Source: SciCrunch Registry

http://bioimaging.dbi.udel.edu

Microscopy facility that houses equipment including confocal microscopes: LSM780 confocal microscope (Located at CBBI),LSM880 confocal microscope (Located at DBI 117),electron microscopes and their accessory instrumentation:Thermo Scientific Apreo VS SEM microscope,Hitachi S-4700, Leica EM ACE600 and Tousimis Autosamdri-815B,CX7 high content analysis system. Our staff has technical expertise across different microscopy platforms and methodologies.

Proper citation: University of Delaware BioImaging Center Core Facility (RRID:SCR_017814) Copy   

•   Source: SciCrunch Registry

https://www.une.edu/research/cobre/histology

Core provides access to expertise, training and specialized instrumentation related to tissue processing, sectioning, staining, immunohistochemistry and microscopy. Offers services and training related to image analysis and image analysis software to guide investigators in choosing best methods for presenting their data.Services include Trimming of wet tissues,Tissue processing into paraffin, OCT and paraffin embedding, Sectioning of paraffin-embedded/frozen tissues, Routine and special histochemical staining, Immunohistochemistry/Immunofluorescence, Antibody optimization, Brightfield/ widefield/ confocal microscopy, Image capture and image analysis.Core has cryostats, microtomes and microscopes available for reservation.

Proper citation: University of New England COBRE Histology and Imaging Core Facility (RRID:SCR_017885) Copy   

•   Source: SciCrunch Registry

http://proteomics.northwestern.edu/collaborate

Core offers multiple types of experiments from simple protein identification to protein quantitation. Performs traditional bottom-up proteomics, where proteins are digested with enzyme prior to analysis and intact, top-down proteomics analyses. Services include proteins identification after in-gel or in-solution digestion, top-down mass spectrometry to preserve post-translationally modified forms of proteins present in vivo by measuring them intact, IP-MS Pulldown,BioID service to identify target of biotin ligase that has been tagged onto their protein via traditional cloning methods,Untargeted Quantitative Peptide Proteomics,Targeted Quantitative Peptide Proteomics,Epiproteomic Histone Modification Panel A,Epiproteomic Histone Modification Panel B,Untargeted Metabolomics,Phosphoproteomics,PTM Scan,ChIP-MS.

Proper citation: Northwestern University Proteomics Core Facility (RRID:SCR_017945) Copy   

•   Source: SciCrunch Registry