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https://www.fdilab.org

UCSD based bioinformatics lab composed of several projects in different biomedical disciplines. Established in 2008 as Neuroscience Information Framework and has since expanded to include broader field of biomedical research. Leader in developing and providing novel informatics infrastructure and tools for making data FAIR: Findable, Accessible, Interoperable and Reusable. FAIR Data informatics laboratory develops SciCrunch.org platform.

Proper citation: FAIR Data Informatics Laboratory (RRID:SCR_019235) Copy   

•   Source: SciCrunch Registry

http://nashua.case.edu/PathwaysWeb/Web/

An integrated software system for storing, managing, analyzing, and querying biological pathways at different levels of genetic, molecular, biochemical and organismal detail. The system contains a pathways database and associated tools to store, compare, query, and visualize metabolic pathways. The aim is to develop an integrated database and the associated tools to support computational analysis and visualization of biochemical pathways. At the computational level, PathCase allows users to visualize pathways in multiple abstraction levels, and to pose predetermined and ad hoc queries using a graphical user interface. Pathways are represented as graphs, and implemented as a relational database. The available functional annotations include the identity of the substrate(s), product(s), cofactors, activators, inhibitors, enzymes or other processing molecules, GO-categories of enzymes (as well as GO hierarchy visualizations two-way-linked to PathCase enzymes), EC number information and the associated links, and synonyms and encoding genes of gene products.

Proper citation: PathCase Pathways Database System (RRID:SCR_001835) Copy   

•   Source: SciCrunch Registry

http://videocast.nih.gov/

VideoCasting of special NIH events, seminars, conferences, meetings and lectures available to viewers on the NIH network and the Internet from the VideoCast web site. VideoCasting is the method of electronically streaming digitally encoded video and audio data from a server to a client. VideoCast is often referred to as streaming video. Streaming files are not downloaded, but rather are broadcast in a manner similar to television broadcasts. The videos are processed by a compression program into a streaming format and delivered in a staggered fashion to minimize impact upon the network and maximize the experience of the content for the viewer. When users request a streaming file they will receive an initial burst of data after a short delay (file latency). While content is being viewed, the streaming server machine and software continues to stream data in such a manner that the viewer experiences no break in the content. CIT can broadcast your seminar, conference or meeting live to a world-wide audience over the Internet as a real-time streaming video. The event can be recorded and made available for viewers to watch at their convenience as an on-demand video or a downloadable podcast. CIT can also broadcast NIH-only or HHS-only content.

Proper citation: NIH VideoCasting (RRID:SCR_001885) Copy   

•   Source: SciCrunch Registry

http://provean.jcvi.org/

A software tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein.

Proper citation: PROVEAN (RRID:SCR_002182) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/gsea/

Software package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.

Proper citation: Gene Set Enrichment Analysis (RRID:SCR_003199) Copy   

•   Source: SciCrunch Registry

http://www.humanconnectomeproject.org/

A multi-center project comprising two distinct consortia (Mass. Gen. Hosp. and USC; and Wash. U. and the U. of Minn.) seeking to map white matter fiber pathways in the human brain using leading edge neuroimaging methods, genomics, architectonics, mathematical approaches, informatics, and interactive visualization. The mapping of the complete structural and functional neural connections in vivo within and across individuals provides unparalleled compilation of neural data, an interface to graphically navigate this data and the opportunity to achieve conclusions about the living human brain. The HCP is being developed to employ advanced neuroimaging methods, and to construct an extensive informatics infrastructure to link these data and connectivity models to detailed phenomic and genomic data, building upon existing multidisciplinary and collaborative efforts currently underway. Working with other HCP partners based at Washington University in St. Louis they will provide rich data, essential imaging protocols, and sophisticated connectivity analysis tools for the neuroscience community. This project is working to achieve the following: 1) develop sophisticated tools to process high-angular diffusion (HARDI) and diffusion spectrum imaging (DSI) from normal individuals to provide the foundation for the detailed mapping of the human connectome; 2) optimize advanced high-field imaging technologies and neurocognitive tests to map the human connectome; 3) collect connectomic, behavioral, and genotype data using optimized methods in a representative sample of normal subjects; 4) design and deploy a robust, web-based informatics infrastructure, 5) develop and disseminate data acquisition and analysis, educational, and training outreach materials.

Proper citation: MGH-USC Human Connectome Project (RRID:SCR_003490) Copy   

•   Source: SciCrunch Registry

http://ccdb.ucsd.edu/SAO

Ontology that describes structures from the dimensional range encompassing cellular and subcellular structure, supracellular domains, and macromolecules. It is built according to ontology development best practices (re-use of existing ontologies; formal definitions of terms; use of foundational ontologies). It describes the parts of neurons and glia and how these parts come together to define supracellular structures such as synapses and neuropil. Molecular specializations of each compartment and cell type are identified. The SAO was designed with the goal of providing a means to annotate cellular and subcellular data obtained from light and electron microscopy, including assigning macromolecules to their appropriate subcellular domains. The SAO thus provides a bridge between ontologies that describe molecular species and those concerned with more gross anatomical scales. Because it is intended to integrate into ontological efforts at these other scales, particular care was taken to construct the ontology in a way that supports such integration.

Proper citation: Subcellular Anatomy Ontology (RRID:SCR_003486) Copy   

•   Source: SciCrunch Registry

https://bioportal.bioontology.org/ontologies/NEMO/?p=summary

Ontology that describes classes of event-related brain potentials (ERP) and their properties, including spatial, temporal, and functional (cognitive / behavioral) attributes, and data-level attributes (acquisition and analysis parameters). Its aim is to support data sharing, logic-based queries and mapping/integration of patterns across data from different labs, experiment paradigms, and modalities (EEG/MEG).

Proper citation: NEMO Ontology (RRID:SCR_003386) Copy   

•   Source: SciCrunch Registry

http://www.med.uc.edu/cardio_bio/

Our 24 faculty members approach the Research and Training in Cardiovascular Biology program from different subspecialties that include genetics, metabolism, development, cellular biology, systems biology, structural biology, biophysics, pharmacology, molecular biology, bioinformatics and biochemistry. While these subspecialties are clearly diverse, our faculty collaboratively leverages these areas toward the common goal of understanding cardiovascular disease from the gene all the way up to integrated organism function (systems biology). This diverse array of subspecialties provides a truly unique training environment that few centers can match. Another critical aspect of our training program is our steadfast commitment to a superior and nurturing training environment for our predoctoral trainees, postdoctoral trainees and clinician-scientists. Our training faculty are uniformly committed to monitoring our personnel for success in every way possible, to not only ensure their future placement in the academic ranks but to also build a stronger cardiovascular community around the country. The current National Institutes of Health-sponsored Research and Training in Cardiovascular Biology was instituted in 1978 by Arnold Schwartz, MD, PhD. This program has trained more than 120 scientists, who have pursued independent research careers and are holding prominent scientific positions worldwide. Our trainees have been distinguished as chairs of basic science departments, directors of centers or pharmaceutical companies, clinical directors and tenured faculty members in academic research. The overall emphasis continues to focus on integrative training and well-rounded knowledge of the fundamentals in biochemical, molecular, physiological and pharmacological underpinnings of cardiovascular disease. Dr. Schwartz has been a constant guiding force since the program was established. The University of Cincinnati, with Cincinnati Children's, has also developed a reputation as a leading center for the generation and analysis of genetically modified mouse models for interrogation of gene-disease relationships in the heart. This theme has been expanded to incorporate molecular genomics, proteomics and bioinformatics, as we continue to be among the leaders in the nation in molecular pathway analysis associated with single gene manipulations in the hearts of mice. Most faculty and trainees are using these approaches, but they are also well-versed in many other aspects of cardiovascular science, including excellence in basic physiology, pharmacology, biochemistry, structural biology and molecular biology. Thus, we are a rare conglomeration of faculty in which all aspects of cardiovascular biology are practiced, starting with cutting-edge molecular and genetic approaches, spanning more traditional cellular and whole animal approaches to build an integrated network of functional and disease-relevant data and extending to translational research incorporating cell therapy.

Proper citation: University of Cincinnati Research and Training in Cardiovascular Biology (RRID:SCR_003860) Copy   

•   Source: SciCrunch Registry

http://www.nih.gov/science/amp/alzheimers.htm

The Alzheimer's disease arm of the Accelerating Medicines Partnership (AMP) that will identify biomarkers that can predict clinical outcomes, conduct a large scale analysis of human AD patient brain tissue samples to validate biological targets, and to increase the understanding of molecular pathways involved in the disease to identify new potential therapeutic targets. The initiative will deposit all data in a repository that will be accessible for use by the biomedical community. The five year endeavor, beginning in 2014, will result in several sets of project outcomes. For the biomarkers project, tau imaging and EEG data will be released in year two, as baseline data becomes available. Completed data from the randomized, blinded trials will be added after the end of the five year studies. This will include both imaging data and data from blood and spinal fluid biomarker studies. For the network analysis project, each project will general several network models of late onset AD (LOAD) and identify key drivers of disease pathogensis by the end of year three. Years four and five will be dedicated to validating the novel targets and refining the network models of LOAD, including screening novel compounds or drugs already in use for other conditions that may have the ability to modulate the likely targets.

Proper citation: Accelerating Medicines Partnership - Alzheimers (RRID:SCR_003742) Copy   

•   Source: SciCrunch Registry

http://elementsofmorphology.nih.gov/

Data set of standardized terms used to describe human morphology including definitions of terms for the craniofacies in general, the major components of the face, and the hands and feet. This provides a uniform and internationally accepted terms to describe the human phenotype.

Proper citation: elements of morphology (RRID:SCR_003707) Copy   

•   Source: SciCrunch Registry

http://www.nih.gov/science/amp/autoimmune.htm

The autoimmune disease arm of the Accelerating Medicine Partnership (AMP), which aims to identify and validate the most promising biological targets of disease for new diagnostic and drug development, that is focused on rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). They seek to identify shared common flaws in inflammation, particularly those that are shared with a larger number of autoimmune disorders which can cause severe disability, greatly affect quality of life, and are associated with an increased risk of death. This project aims to reveal biomarkers and biological targets for drug development, matching existing drugs to patients with specific molecular profiles who are most likely to benefit. The research plan proposes a 5 year process. Year one will include startup activities such as validation of tissue acquisition processes and analytic technologies, and the development of operating procedures. The second year will focus on identification of disease specific pathways by comparing data from patients and healthy individuals. Years 3-5 will expand the scale to include comparisons of different subsets of patients with RA or lupus to allow molecularly based patient stratification for precise treatment. The final 12 months (2019) will also include preliminary target validation. The data will be made publicly available through an internet-based information portal.

Proper citation: Accelerating Medicines Partnership Autoimmune Diseases of Rheumatoid Arthritis and Lupus (RRID:SCR_003731) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/pmc/

Collection of full text archive of biomedical and life sciences journal literature at U.S. National Institutes of Health National Library of Medicine (NIH/NLM). With PubMed Central, NCBI is taking lead in preserving and maintaining open access to electronic literature. Value of PubMed Central, in addition to its role as an archive, lies in what can be done when data from diverse sources is stored in common format in single repository. All articles in PMC are free (sometimes on a delayed basis). Some journals go beyond free, to Open Access.

Proper citation: PubMed Central (RRID:SCR_004166) Copy   

•   Source: SciCrunch Registry

https://neurowiki.case.edu/

Curriculum materials for an Introduction to Neurobiology course for undergraduate and graduate students.

The course focuses on the analysis of neurons and neural circuits for behavior using the fundamental principles of neuroscience. From the online course syllabus, the 24 units that make up the course may be directly accessed. Each unit contains a reading, links to at least one simulation, and a problem set.

A list of all available simulations can be found here: https://neurowiki.case.edu/wiki/Simulations. * 25 simulations are written in JavaScript and will run in any browser.
Source code: https://github.com/CWRUChielLab/JSNeuroSim * Pre-compiled executables (Windows, Mac, Linux) are available for 1 desktop simulation, the Nernst Potential Simulator.
Source code: https://github.com/CWRUChielLab/Nernst Structure of the Course * Solving problems based on simulations of neuronal components, neurons, and simple circuits to understand how they work. * For advanced students, writing a neuroscience Wikipedia article, critical review, or grant, in stages.

Proper citation: NeuroWiki (RRID:SCR_004066) Copy   

•   Source: SciCrunch Registry

http://chavi-id.org

A consortium whose goal is to further HIV research and accelerate the development of a preventative HIV vaccine. Its main research target is to define immunogens and immunization regimens that induce sustained HIV cross-protective B cell and CD4+ T cell responses.

Proper citation: CHAVI-ID (RRID:SCR_014047) Copy   

•   Source: SciCrunch Registry

https://datajoint.org/

MATLAB and Python 3 high-level programming interface for MySQL databases to support data processing chains in science labs. Specifically designed to provide robust and intuitive data model for scientific data processing chains.Used for scientific data pipelines and workflow management.

Proper citation: DataJoint (RRID:SCR_014543) Copy   

•   Source: SciCrunch Registry

http://i2b2.cchmc.org/

A data warehouse that integrates information on patients from multiple sources and consists of patient information from all the visits to Cincinnati Children''''s between 2003 and 2007. This information includes demographics (age, gender, race), diagnoses (ICD-9), procedures, medications and lab results. They have included extracts from Epic, DocSite, and the new Cerner laboratory system and will eventually load public data sources, data from the different divisions or research cores (such as images or genetic data), as well as the research databases from individual groups or investigators. This information is aggregated, cleaned and de-identified. Once this process is complete, it is presented to the user, who will then be able to query the data. The warehouse is best suited for tasks like cohort identification, hypothesis generation and retrospective data analysis. Automated software tools will facilitate some of these functions, while others will require more of a manual process. The initial software tools will be focused around cohort identification. They have developed a set of web-based tools that allow the user to query the warehouse after logging in. The only people able to see your data are those to whom you grant authorization. If the information can be provided to the general research community, they will add it to the warehouse. If it cannot, they will mark it so that only you (or others in your group with proper approval) can access it.

Proper citation: i2b2 Research Data Warehouse (RRID:SCR_013276) Copy   

•   Source: SciCrunch Registry

https://www.immport.org/home

Data sharing repository of clinical trials, associated mechanistic studies, and other basic and applied immunology research programs. Platform to store, analyze, and exchange datasets for immune mediated diseases. Data supplied by NIAID/DAIT funded investigators and genomic, proteomic, and other data relevant to research of these programs extracted from public databases. Provides data analysis tools and immunology focused ontology to advance research in basic and clinical immunology.

Proper citation: The Immunology Database and Analysis Portal (ImmPort) (RRID:SCR_012804) Copy   

•   Source: SciCrunch Registry

http://www.metabolomicsworkbench.org

Repository for metabolomics data and metadata which provides analysis tools and access to various resources. NIH grantees may upload data and general users can search metabolomics database. Provides protocols for sample preparation and analysis, information about NIH Metabolomics Program, data sharing guidelines, funding opportunities, services offered by its Regional Comprehensive Metabolomics Resource Cores (RCMRC)s, and training workshops.

Proper citation: Metabolomics Workbench (RRID:SCR_013794) Copy   

•   Source: SciCrunch Registry

http://www.geworkbench.org

geWorkbench (genomics Workbench) is a Java-based open-source platform for integrated genomics. Using a component architecture it allows individually developed plug-ins to be configured into complex bioinformatic applications. At present there are more than 70 available plug-ins supporting the visualization and analysis of gene expression and sequence data. Example use cases include: * loading data from local or remote data sources. * visualizing gene expression, molecular interaction networks, protein sequence and protein structure data in a variety of ways. * providing access to client- and server-side computational analysis tools such as t-test analysis, hierarchical clustering, self organizing maps, regulatory networks reconstruction, BLAST searches, pattern/motif discovery, etc. * validating computational hypothesis through the integration of gene and pathway annotation information from curated sources as well as through Gene Ontology enrichment analysis. geWorkbench is the Bioinformatics platform of MAGNet, the National Center for the Multi-scale Analysis of Genomic and Cellular Networks (one of the 7 National Centers for Biomedial Computing funded through the NIH Roadmap). Additionally, geWorkbench is supported by caBIG, NCI''s cancer Biomedical Informatics Grid initiative.

Proper citation: genomics Workbench (RRID:SCR_013599) Copy   

•   Source: SciCrunch Registry