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http://www.na-mic.org/

The National Alliance for Medical Image Computing (NA-MIC) is a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who develop computational tools for the analysis and visualization of medical image data. The purpose of the Center is to provide the infrastructure and environment for the development of computational algorithms and open-source technologies, and then oversee the training and dissemination of these tools to the medical research community. Electronic resources provided by NA-MIC include software, data, tutorials, presentations, and more.

Proper citation: National Alliance for Medical Image Computing (RRID:SCR_004460) Copy   

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

Database that provides access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez. A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. BioSystem records list and categorize components, such as the genes, proteins, and small molecules involved in a biological system. The companion FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems. A number of databases provide diagrams showing the components and products of biological pathways along with corresponding annotations and links to literature. This database was developed as a complementary project to (1) serve as a centralized repository of data; (2) connect the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system; and (3) facilitate computation on biosystems data. The NCBI BioSystems Database currently contains records from several source databases: KEGG, BioCyc (including its Tier 1 EcoCyc and MetaCyc databases, and its Tier 2 databases), Reactome, the National Cancer Institute's Pathway Interaction Database, WikiPathways, and Gene Ontology (GO). It includes several types of records such as pathways, structural complexes, and functional sets, and is desiged to accomodate other record types, such as diseases, as data become available. Through these collaborations, the BioSystems database facilitates access to, and provides the ability to compute on, a wide range of biosystems data. If you are interested in depositing data into the BioSystems database, please contact them.

Proper citation: NCBI BioSystems Database (RRID:SCR_004690) Copy   

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http://www.proconsortium.org/pro/

An ontological representation of protein-related entities by explicitly defining them and showing the relationships between them. Each PRO term represents a distinct class of entities (including specific modified forms, orthologous isoforms, and protein complexes) ranging from the taxon-neutral to the taxon-specific. The ontology has a meta-structure encompassing three areas: proteins based on evolutionary relatedness (ProEvo); protein forms produced from a given gene locus (ProForm); and protein-containing complexes (ProComp). NOTICE: The PRO ID format has changed from PRO: to PR: (e.g. PRO:000000563 is now PR:000000563).

Proper citation: PR (RRID:SCR_004964) Copy   

•   Source: SciCrunch Registry

http://www.hmpdacc.org/

Common repository for diverse human microbiome datsets and minimum reporting standards for Common Fund Human Microbiome Project.

Proper citation: HMP Data Analysis and Coordination Center (RRID:SCR_004919) Copy   

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http://gds.nih.gov/

NIH established expectations for sharing data obtained through NIH-funded genome-wide association studies (GWAS) with the implementation of the GWAS Policy. Information and resources related to the GWAS Policy can be found on this website.

Proper citation: Genomic Datasharing (RRID:SCR_005233) Copy   

•   Source: SciCrunch Registry

http://www.ncbcs.org/

The NCBCs are a set of networked computation core facilities for biomedical infrastructure funded under the NIH Common Fund, the National Program of Excellence in Biomedical Computing (NPEBC). There are seven funded Centers that cover systems biology, image processing, biophysical modeling, biomedical ontologies, information integration, and tools for gene-phenotype and disease analysis. The centers will create innovative software programs and other tools that enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease. Each Center has Cores that are focused on (1) computational science, (2) biomedical computational science and (3) driving biological projects whose intent is to drive the interaction between computational and biomedical computational science. In addition to the Centers, the NIH has a number of active program announcements to develop collaborations with the biomedical research community��this includes announcements from the Biomedical Information Science and Technology Initiative (BISTI) and the Program for Collaborations with National Centers for Biomedical Computing. There are numerous efforts in education and training that emanate from the Centers and there is an annual all hands meeting.

Proper citation: National Centers for Biomedical Computing (RRID:SCR_010635) Copy   

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https://drugs.ncats.io/

Portal of NCATS (the National Center for Advancing Translational Sciences) for drug development information including:US approved drugs, marketed drugs, investigational drugs. Provides manually curated data supplied by the FDA and private companies. Provides drugs marketing and regulatory status, drug ingredient definitions, biological activity and clinical use.

Proper citation: Inxight (RRID:SCR_016490) Copy   

•   Source: SciCrunch Registry

https://www.niaid.nih.gov/about/cyber-infrastructure-computational-biology-contacts

Office of Cyber Infrastructure and Computational Biology of NIAID.

Proper citation: OCICB (RRID:SCR_016597) Copy   

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https://www.ebi.ac.uk/intact/complex/#annotations:fIzBXhJPEeej78Pl6R0ScA

Database and encyclopaedic resource of macromolecular complexes found in key model organisms from scientific literature. Data includes protein-only complexes, protein-small molecules, and protein-nucleic acid complexes. The information within the portal is manually curated and available for download.

Proper citation: Complex Portal (RRID:SCR_015038) Copy   

•   Source: SciCrunch Registry

https://www.jax.org/news-and-insights/2013/february/komp2-mice-phenotyping-and-availability

Knockout Mouse Phenotyping Project, JAX information about their contributions to KOMP2 project. Project to generate and phenotype single gene KO mouse strains from KOMP ES cell lines. Strains are phenotyped using protocols in pipeline designed by International Mouse Phenotyping Consortium. There are three NIH-funded phenotyping centers in United States: JAX, BaSH Consortium (Baylor College of Medicine, the Wellcome Trust Sanger Institute and MRC Harwell), and the DTCC Consortium (University of California at Davis, the Toronto Center for Phenogenomics, Children’s Hospital Oakland Research Institute (CHORI) and Charles River ).

Proper citation: KOMP2 (RRID:SCR_017528) Copy   

•   Source: SciCrunch Registry

https://scienceofbehaviorchange.org/about/

Repository for behavioral science measures that have been validated or are in process of being validated in accordance with SOBC Experimental Medicine Approach.

Proper citation: Science of Behavior Change Research Network (RRID:SCR_017385) Copy   

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https://www.portal.brain-bican.org/

Provides molecular and anatomical foundational framework for study of brain function and disorders.Comprehensive Center on Human and Non-Human Primate Brain Cell Atlases with goal to build reference brain cell atlases that will be used throughout research community.

Proper citation: BRAIN Initiative Cell Atlas Network (RRID:SCR_022794) Copy   

•   Source: SciCrunch Registry

http://www.mitomap.org/

Database of polymorphisms and mutations of the human mitochondrial DNA. It reports published and unpublished data on human mitochondrial DNA variation. All data is curated by hand. If you would like to submit published articles to be included in mitomap, please send them the citation and a pdf.

Proper citation: MITOMAP - A human mitochondrial genome database (RRID:SCR_002996) Copy   

•   Source: SciCrunch Registry

http://mistdb.com

Database which contains the signal transduction proteins for complete and draft bacterial and archaeal genomes. The MiST2 database identifies and catalogs the repertoire of signal transduction proteins in microbial genomes.

Proper citation: MiST - Microbial Signal Transduction database (RRID:SCR_003166) Copy   

•   Source: SciCrunch Registry

http://integromedb.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. Search engine that integrates over 100 curated and publicly contributed data sources and provides integrated views on the genomic, proteomic, transcriptomic, genetic and functional information currently available. Information featured in the database includes gene function, orthologies, gene expression, pathways and protein-protein interactions, mutations and SNPs, disease relationships, related drugs and compounds.

Proper citation: IntegromeDB (RRID:SCR_004620) Copy   

•   Source: SciCrunch Registry

http://bigg.ucsd.edu/

A knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.

Proper citation: BiGG Database (RRID:SCR_005809) Copy   

•   Source: SciCrunch Registry

http://www.phenologs.org/

Database for identifying orthologous phenotypes (phenologs). Mapping between genotype and phenotype is often non-obvious, complicating prediction of genes underlying specific phenotypes. This problem can be addressed through comparative analyses of phenotypes. We define phenologs based upon overlapping sets of orthologous genes associated with each phenotype. Comparisons of >189,000 human, mouse, yeast, and worm gene-phenotype associations reveal many significant phenologs, including novel non-obvious human disease models. For example, phenologs suggest a yeast model for mammalian angiogenesis defects and an invertebrate model for vertebrate neural tube birth defects. Phenologs thus create a rich framework for comparing mutational phenotypes, identify adaptive reuse of gene systems, and suggest new disease genes. To search for phenologs, go to the basic search page and enter a list of genes in the box provided, using Entrez gene identifiers for mouse/human genes, locus ids for yeast (e.g., YHR200W), or sequence names for worm (e.g., B0205.3). It is expected that this list of genes will all be associated with a particular system, trait, mutational phenotype, or disease. The search will return all identified model organism/human mutational phenotypes that show any overlap with the input set of the genes, ranked according to their hypergeometric probability scores. Clicking on a particular phenolog will result in a list of genes associated with the phenotype, from which potential new candidate genes can identified. Currently known phenotypes in the database are available from the link labeled ''Find phenotypes'', where the associated gene can be submitted as queries, or alternately, can be searched directly from the link provided.

Proper citation: Phenologs (RRID:SCR_005529) Copy   

•   Source: SciCrunch Registry

http://lussierlab.org/GO-Module/GOModule.cgi

GO-Module provides an interface to reduce the dimensionality of GO enrichment results and produce interpretable biomodules of significant GO terms organized by hierarchical knowledge that contain only true positive results. Users can download a text file of GO terms annotated with their significance and identified biomodules, a network visualization of resultant GO IDs or terms in PDF format, and view results in an online table. Platform: Online tool

Proper citation: GO-Module (RRID:SCR_005813) Copy   

•   Source: SciCrunch Registry

https://mskkp.org/

Portal enables browsing, searching, and analysis of human genetic and genomic information linked to musculoskeletal traits and diseases, while protecting the integrity and confidentiality of underlying data.

Proper citation: Musculoskeletal Knowledge Portal (RRID:SCR_023171) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE, as of October 1, 2013; however, the site is still accessible. NCBI resource that contains sequence and mapping data on short genomic landmark sequences or Sequence Tagged Sites. STS sequences are incorporated into the STS Division of GenBank. The dbSTS database offers a route for submission of STS sequences to GenBank. It is designed especially for the submission of large batches of STS sequences.

Proper citation: dbSTS (RRID:SCR_000400) Copy   

•   Source: SciCrunch Registry