Tools   Select Another Resource Report Type

http://mimi.ncibi.org/MimiWeb/main-page.jsp

MiMi Web gives you an easy to use interface to a rich NCIBI data repository for conducting your systems biology analyses. This repository includes the MiMI database, PubMed resources updated nightly, and text mined from biomedical research literature. The MiMI database comprehensively includes protein interaction information that has been integrated and merged from diverse protein interaction databases and other biological sources. With MiMI, you get one point of entry for querying, exploring, and analyzing all these data. MiMI provides access to the knowledge and data merged and integrated from numerous protein interactions databases and augments this information from many other biological sources. MiMI merges data from these sources with deep integration into its single database with one point of entry for querying, exploring, and analyzing all these data. MiMI allows you to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI displays results of your queries in easy-to-browse interfaces and provides you with workspaces to explore and analyze the results. Among these workspaces is an interactive network of protein-protein interactions displayed in Cytoscape and accessed through MiMI via a MiMI Cytoscape plug-in. MiMI gives you access to more information than you can get from any one protein interaction source such as: * Vetted data on genes, attributes, interactions, literature citations, compounds, and annotated text extracts through natural language processing (NLP) * Linkouts to integrated NCIBI tools to: analyze overrepresented MeSH terms for genes of interest, read additional NLP-mined text passages, and explore interactive graphics of networks of interactions * Linkouts to PubMed and NCIBI's MiSearch interface to PubMed for better relevance rankings * Querying by keywords, genes, lists or interactions * Provenance tracking * Quick views of missing information across databases. Data Sources include: BIND, BioGRID, CCSB at Harvard, cPath, DIP, GO (Gene Ontology), HPRD, IntAct, InterPro, IPI, KEGG, Max Delbreuck Center, MiBLAST, NCBI Gene, Organelle DB, OrthoMCL DB, PFam, ProtoNet, PubMed, PubMed NLP Mining, Reactome, MINT, and Finley Lab. The data integration service is supplied under the conditions of the original data sources and the specific terms of use for MiMI. Access to this website is provided free of charge. The MiMI data is queryable through a web services api. The MiMI data is available in PSI-MITAB Format. These files represent a subset of the data available in MiMI. Only UniProt and RefSeq identifiers are included for each interactor, pathways and metabolomics data is not included, and provenance is not included for each interaction. If you need access to the full MiMI dataset please send an email to mimi-help (at) umich.edu.

Proper citation: Michigan Molecular Interactions (RRID:SCR_003521) Copy   

•   Source: SciCrunch Registry

http://cng.gmu.edu:8080/Lm

A freely available software tool available for the Windows and Linux platform, as well as the Online version Applet, for the analysis, comparison and search of digital reconstructions of neuronal morphologies. For the quantitative characterization of neuronal morphology, LM computes a large number of neuroanatomical parameters from 3D digital reconstruction files starting from and combining a set of core metrics. After more than six years of development and use in the neuroscience community, LM enables the execution of commonly adopted analyses as well as of more advanced functions, including: (i) extraction of basic morphological parameters, (ii) computation of frequency distributions, (iii) measurements from user-specified subregions of the neuronal arbors, (iv) statistical comparison between two groups of cells and (v) filtered selections and searches from collections of neurons based on any Boolean combination of the available morphometric measures. These functionalities are easily accessed and deployed through a user-friendly graphical interface and typically execute within few minutes on a set of 20 neurons. The tool is available for either online use on any Java-enabled browser and platform or may be downloaded for local execution under Windows and Linux.

Proper citation: L-Measure (RRID:SCR_003487) Copy   

•   Source: SciCrunch Registry

http://neuroscienceblueprint.nih.gov/

Collaborative framework that includes the NIH Office of the Director and the 14 NIH Institutes and Centers that support research on the nervous system. By pooling resources and expertise, the Blueprint identifies cross-cutting areas of research, and confronts challenges too large for any single Institute or Center. The Blueprint makes collaboration a day-to-day part of how the NIH does business in neuroscience, complementing the basic missions of Blueprint partners. During each fiscal year, the partners contribute a small percentage of their funds to a common pool. Since the Blueprint's inception in 2004, this pool has comprised less than 1 percent of the total neuroscience research budget of the partners. In 2009, the Blueprint Grand Challenges were launched to catalyze research with the potential to transform our basic understanding of the brain and our approaches to treating brain disorders. * The Human Connectome Project is an effort to map the connections within the healthy brain. It is expected to help answer questions about how genes influence brain connectivity, and how this in turn relates to mood, personality and behavior. The investigators will collect brain imaging data, plus genetic and behavioral data from 1,200 adults. They are working to optimize brain imaging techniques to see the brain's wiring in unprecedented detail. * The Grand Challenge on Pain supports research to understand the changes in the nervous system that cause acute, temporary pain to become chronic. The initiative is supporting multi-investigator projects to partner researchers in the pain field with researchers in the neuroplasticity field. * The Blueprint Neurotherapeutics Network is helping small labs develop new drugs for nervous system disorders. The Network provides research funding, plus access to millions of dollars worth of services and expertise to assist in every step of the drug development process, from laboratory studies to preparation for clinical trials. Project teams across the U.S. have received funding to pursue drugs for conditions from vision loss to neurodegenerative disease to depression. Since its inception in 2004, the Blueprint has supported the development of new resources, tools and opportunities for neuroscientists. For example, the Blueprint supports several training programs to help students pursue interdisciplinary areas of neuroscience, and to bring students from underrepresented groups into the neurosciences. The Blueprint also funds efforts to develop new approaches to teaching neuroscience through K-12 instruction, museum exhibits and web-based platforms. From fiscal years 2007 to 2009, the Blueprint focused on three major themes of neuroscience - neurodegeneration, neurodevelopment, and neuroplasticity. These efforts enabled unique funding opportunities and training programs, and helped establish new resources including the Blueprint Non-Human Primate Brain Atlas.

Proper citation: NIH Blueprint for Neuroscience Research (RRID:SCR_003670) Copy   

•   Source: SciCrunch Registry

http://www.oae-ontology.org

Biomedical ontology in the domain of adverse events that aims to standardize adverse event annotation, integrate various adverse event data, and support computer-assisted reasoning. AEO is a community-based ontology. Its development follows the OBO Foundry principles.

Proper citation: Ontology of Adverse Events (RRID:SCR_003428) Copy   

•   Source: SciCrunch Registry

http://www.mindtouch.com/

A web based social authoring and publishing environment that adheres to open standards and RESTful design principals. It provides wiki-like ease of use with a sophisticated web services framework for rapid application development, creating flexible workflows and rapid integration. MindTouch creates a vibrant real-time information fabric by federating content from across enterprise silos, such as CRM, ERP, file servers, email, databases, web services and more.

Proper citation: Mindtouch DekiWiki (RRID:SCR_003425) Copy   

•   Source: SciCrunch Registry

http://www.addconsortium.org/

A collaborative network among university-led drug discovery centers and programs to allow scientists to exchange technical expertise on drug discovery and development strategies as well as form partnerships with each other, biopharma companies, and drug discovery-focused contract service organizations and consultants. The website will also serve as a repository for drug discovery events, educational material, job postings, and partnership opportunities. Through active member participation this website will become a valuable tool for every scientist working in the drug discovery arena. In addition, involvement of members will enable them to effectively advocate to the NIH and other funding agencies to increase the awareness of the growing number of academic drug discovery scientists and their success as well as their needs.

Proper citation: Academic Drug Discovery Consortium (RRID:SCR_003706) Copy   

•   Source: SciCrunch Registry

http://www.nanostring.com/products/nSolver

Data analysis software program that offers nCounter users the ability to QC, normalize, and analyze data without having to purchase additional software packages.

Proper citation: nSolver Analysis Software (RRID:SCR_003420) Copy   

•   Source: SciCrunch Registry

http://graphml.graphdrawing.org/

A file format for graphs that consists of a language core to describe the structural properties of a graph and a flexible extension mechanism to add application-specific data. It is based on XML and is ideally suited as a common denominator for all kinds of services generating, archiving, or processing graphs. Its main features include support of * directed, undirected, and mixed graphs, * hypergraphs, * hierarchical graphs, * graphical representations, * references to external data, * application-specific attribute data, and * light-weight parsers.

Proper citation: GraphML (RRID:SCR_003545) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/efo/

An application focused ontology modelling the experimental factors in ArrayExpress and Gene Expression Atlas. It has been developed to increase the richness of the annotations that are currently made in the ArrayExpress repository, to promote consistent annotation, to facilitate automatic annotation and to integrate external data. The ontology describes cross-product classes from reference ontologies in area such as disease, cell line, cell type and anatomy. The methodology employed in the development of EFO involves construction of mappings to multiple existing domain specific ontologies, such as the Disease Ontology and Cell Type Ontology. This is achieved using a combination of automated and manual curation steps and the use of a phonetic matching algorithm. The ontology is evaluated with use cases from the ArrayExpress repository and ArrayExpress Atlas. You may also browse the EFO in the NCBO Bioportal. Term submissions are welcome.

Proper citation: Experimental Factor Ontology (RRID:SCR_003574) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/adverse-event-reporting-ontology/

An ontology aimed at supporting clinicians at the time of data entry, increasing quality and accuracy of reported adverse events.

Proper citation: Adverse Event Reporting Ontology (RRID:SCR_003571) Copy   

•   Source: SciCrunch Registry

http://www.isi.edu/integration/karma/

An information integration software tool that enables users to integrate data from a variety of data sources including databases, spreadsheets, delimited text files, XML, JSON, KML and Web APIs. Users integrate information by modeling it according to an ontology of their choice using a graphical user interface that automates much of the process. Karma learns to recognize the mapping of data to ontology classes and then uses the ontology to propose a model that ties together these classes. Users then interact with the system to adjust the automatically generated model. During this process, users can transform the data as needed to normalize data expressed in different formats and to restructure it. Once the model is complete, users can publish the integrated data as RDF or store it in a database.

Proper citation: Karma (RRID:SCR_003732) Copy   

•   Source: SciCrunch Registry

http://www.asf.alaska.edu/

Satellite facility that downlinks, processes, archives, and distributes remote-sensing data to scientific users around the world. Three major components: * Satellite Tracking Ground Station: Part of NASA?s Near Earth Network system of ground stations around the world. * Synthetic Aperture Radar Distributed Active Archive Center (SAR DAAC): ASF maintains the NASA archive of SAR data from a variety of satellites and aircraft, and provides these data and associated specialty support services to U.S. Government-approved researchers in support of NASA?s Earth Science Data and Information System project. * ASF Enterprise Center (ASFE): In support of UAF?s mission to be a student-centered research university, the ASF-E focuses on applications of remote-sensing data, specifically for UAF research. The ASF-E includes the GeoData Center (GDC), which provides data management and archive services for UAF principal investigators and maintains a variety of geophysical data collections in support of scientific research.

Proper citation: Alaska Satellite Facility (RRID:SCR_003610) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu/tools/ontology/ont_search.cgi

Ontology that defines hierarchical display of different rat strains as derived from parental strains. Ontology Browser allows to retrieve all genes, QTLs, strains and homologs annotated to particular term. Covers all types of biological pathways including altered and disease pathways, and to capture relationships between them within hierarchical structure. Five nodes of ontology include classic metabolic, regulatory, signaling, drug and disease pathways. Ontology allows for standardized annotation of rat. Serves as vehicle to connect between genes and ontology reports, between reports and interactive pathway diagrams, between pathways that directly connect to one another within diagram or between pathways that in some fashion are globally related in pathway suites and suite networks.

Proper citation: Rat Strain Ontology (RRID:SCR_003449) Copy   

•   Source: SciCrunch Registry

https://github.com/alyssafrazee/polyester

An R package designed to simulate RNA sequencing experiments with differential transcript expression. Given a set of annotated transcripts, it will simulate the steps of an RNA-seq experiment (fragmentation, reverse-complementing, and sequencing) and produce files containing simulated RNA-seq reads. Simulated reads can be analyzed using a choice of downstream analysis tools. Polyester has a built-in wrapper function to simulate a case/control experiment with differential transcript expression and biological replicates. Users are able to set the levels of differential expression at transcripts of their choosing. This means they know which transcripts are differentially expressed in the simulated dataset, so accuracy of statistical methods for differential expression detection can be analyzed. Polyester offers several unique features: * Built-in functionality to simulate differential expression at the transcript level * Ability to explicitly set differential expression signal strength * Simulation of small datasets, since large RNA-seq datasets can require lots of time and computing resources to analyze * Generation of raw RNA-seq reads, as opposed to alignments or transcript-level abundance estimates * Transparency/open-source code

Proper citation: Polyester (RRID:SCR_003602) Copy   

•   Source: SciCrunch Registry

https://www.projectdatasphere.org/

Initiative to advance oncology research by enabling collaborative sharing of historical oncology clinical trial data through a universal platform (database). The initiative aims to network all stakeholders in the cancer community researchers, industry, academia, advocacy, and other organizations to share insights and collaborate on issues that could not be solved individually. To do this, they have made efforts to address issues of data privacy, security, intellectual property, resources, and incentives as part of its effort to maximize participation. Data contributions include control arms of clinical trials, and the platform uses data-security precautions and analytics to pool multiple studies associated with the same diagnosis in a manner that seeks to protect the privacy of patients and the security of the data contributed.

Proper citation: Project Data Sphere (RRID:SCR_003726) Copy   

•   Source: SciCrunch Registry

http://www.violinet.org/ovae/

A biomedical ontology in the area of vaccine adverse events aimed to represent and analyze various vaccine-specific adverse events. OVAE is an extension of the Ontology of Adverse Events (OAE) and the Vaccine Ontology (VO).

Proper citation: Ontology of Vaccine Adverse Events (RRID:SCR_003442) Copy   

•   Source: SciCrunch Registry

http://ncit.nci.nih.gov/

A reference terminology and core biomedical ontology for NCI that covers approximately 100,000 key biomedical concepts with terms, codes, definitions, and more than 200,000 inter-concept relationships. It is the reference terminology for NCI, NCI Metathesaurus and NCI informatics infrastructure covering vocabulary for clinical care, translational and basic research, and public information and administrative activities. It includes broad coverage of the cancer domain, including cancer related diseases, findings and abnormalities; anatomy; agents, drugs and chemicals; genes and gene products and so on. In certain areas, like cancer diseases and combination chemotherapies, it provides the most granular and consistent terminology available. It combines terminology from numerous cancer research related domains, and provides a way to integrate or link these kinds of information together through semantic relationships. NCIt features: * Stable, unique codes for biomedical concepts; * Preferred terms, synonyms, definitions, research codes, external source codes, and other information; * Links to NCI Metathesaurus and other information sources; * Over 200,000 cross-links between concepts, providing formal logic-based definition of many concepts; * Extensive content integrated from NCI and other partners, much available as separate NCIt subsets * Updated frequently by a team of subject matter experts. NCIt is a widely recognized standard for biomedical coding and reference, used by a broad variety of public and private partners both nationally and internationally including the Clinical Data Interchange Standards Consortium Terminology (CDISC), the U.S. Food and Drug Administration (FDA), the Federal Medication Terminologies (FMT), and the National Council for Prescription Drug Programs (NCPDP).

Proper citation: NCI Thesaurus (RRID:SCR_003563) Copy   

•   Source: SciCrunch Registry

http://www.themmrf.org/research-programs/commpass-study/

A personalized medicine initiative to discover biomarkers that can better define the biological basis of multiple myeloma to help stratify patients. This effort hopes to obtain samples from approximately 1,000 multiple myeloma patients and follow them over time to identify how a patient's genetic profile is related to clinical progression and treatment response. As a partnership between 17 academic centers, 5 pharmaceuticals and the Department of Veterans Affairs, the goal of this eight year study is to create a database that can accelerate future clinical trials and personalized treatment strategies. MMRF's CoMMpass Study has the following goals: * Create a guide to which treatments work best for specific patient subgroups. * Share data with researchers to accelerate drug development for specific subtypes of multiple myeloma patients. In order to facilitate discoveries and development related to targeted therapies, the comprehensive data from CoMMpass is placed in an open-access research portal. The data will be part of the Multiple Myeloma Research Foundation's (MMRF) Personalized Medicine Platform combines CoMMpass data with those collected from MMRF's Genomics Initiative. It is hoped that the longitudinal data, combined with the annotated bio-specimens will help provide insights that can accelerate personalized therapies.

Proper citation: MMRF CoMMpass Study (RRID:SCR_003721) Copy   

•   Source: SciCrunch Registry

http://www.curealzfund.org/

Cure Alzheimer's Fund is a 501(c)(3) public charity. At Cure Alzheimer's Fund, our mission is to fund research with the highest probability of slowing, stopping or reversing Alzheimer's disease. This topical portal has a lot of information including news and blog. Cure Alzheimer's Fund is governed by a board of directors; administered by a small, full-time staff; and guided scientifically by a Research Consortium. A Scientific Advisory Board audits the research program to make sure it is consistent with the objectives of the foundation. Cure Alzheimer's Fund is a doing business as name for the Alzheimer's Disease Research Foundation, federal tax ID # 52-2396428.

Proper citation: Cure Alzheimers Fund (RRID:SCR_003564) Copy   

•   Source: SciCrunch Registry

http://www.physiol.ucl.ac.uk/research/silver_a/nclamp/

Data acquisition software that runs in conjunction with neuromatic. configured to work with Igor Pro on PC or Mac, instrutech or national instrument acquisition devices. Funded by The Medical Research Council (UK) Compatibility with WaveMetrics Igor Pro 5 and 6. Compatibility with Mac or PC. NIDAQ interfacing multifunction DAQ boards from National Instruments. Requires Igor NIDAQ Tools MX. ITC interfacing - data acquisition systems from InstruTech / Heka. Requires Igor ITC XOPs. Episodic acquisition (stim and sample). Continuous acquisition (currently for ITC users only). Online analysis, with ability to create your own analysis functions. Notes and Log Files which can be displayed within a table or notebook. Automatic saving of data and log folders to your hard drive. Flexible stimulus pulse generator with ability to use your own pulse waveforms. Instant accessibility to all of NeuroMatics and Igor Pros pre-existing data analysis functions.

Proper citation: Nclamp - data acquisition software for electrophysiology (RRID:SCR_003750) Copy   

•   Source: SciCrunch Registry