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https://array.nci.nih.gov/caarray/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on Sep 18, 2018. Open-source, web and programmatically accessible microarray data management system. caArray guides the annotation and exchange of array data using a federated model of local installations whose results are shareable across the cancer Biomedical Informatics Grid (caBIG). caArray furthers translational cancer research through acquisition, dissemination and aggregation of semantically interoperable array data to support subsequent analysis by tools and services on and off the Grid. As array technology advances and matures, caArray will extend its logical library of assay management.

Proper citation: caArray (RRID:SCR_006053) Copy   

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http://www.cancer.gov/aboutnci/office-of-director

Proper citation: Office of the NCI Director (RRID:SCR_004391) Copy   

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http://cgap.nci.nih.gov/

Project to determine the gene expression profiles of normal, precancer, and cancer cells, whose generated resources are available to the cancer community. Interconnected modules provide access to all CGAP data, bioinformatic analysis tools, and biological resources allowing the user to find in silico answers to biological questions in a fraction of the time it once took in the laboratory. * Genes * Tissues * Pathways * RNAi * Chromosomes * SAGE Genie * Tools

Proper citation: Cancer Genome Anatomy Project (RRID:SCR_003072) Copy   

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http://www.dtp.nci.nih.gov

Portal for preclinical information and research materials, including web-accessible data and tools, NCI-60 Tumor Cell Line Screen, compounds in vials and plates, tumor cells, animals, and bulk drugs for investigational new drug (IND)-directed studies. DTP has been involved in the discovery or development of more than 70 percent of the anticancer therapeutics on the market today, and will continue helping the academic and private sectors to overcome various therapeutic development barriers, particularly through supporting high-risk projects and therapeutic development for rare cancers. Initially DTP made its drug discovery and development services and the results from the human tumor cell line assay publicly accessible to researchers worldwide. At first, the site offered in vitro human cell line data for a few thousand compounds and in vitro anti-HIV screening data for roughly 42,000 compounds. Today, visitors can find: * Downloadable in vitro human tumor cell line data for some 43,500 compounds and 15,000 natural product extracts * Results for 60,000 compounds evaluated in the yeast assay * In vivo animal model results for 30,000 compounds * 2-D and 3-D chemical structures for more than 200,000 compounds * Molecular target data, including characterizations for at least 1,200 targets, plus data from multiple cDNA microarray projects In addition to browsing DTP's databases and downloading data, researchers can request individual samples or sets of compounds on 96-well plates for research, or they can submit their own compounds for consideration for screening via DTP's online submission form. Once a compound is submitted for screening, researchers can follow its progress and retrieve data using a secure web interface. The NCI has collected information on almost half a million chemical structures in the past 50 years. DTP has made this information accessible and useful for investigators through its 3-D database, a collection of three-dimensional structures for more than 200,000 drugs. Investigators use the 3-D database to screen compounds for anticancer therapeutic activity. Also available on DTP's website are 127,000 connection tables for anticancer agents. A connection table is a convenient way of depicting molecular structures without relying on drawn chemical structures. As unique lists of atoms and their connections, the connection tables can be indexed and stored in computer databases where they can be used for patent searches, toxicology studies, and precursor searching, for example., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Developmental Therapeutics Program (RRID:SCR_003057) Copy   

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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   

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http://ctakes.apache.org

An open-source natural language processing system for information extraction from electronic medical record clinical free-text. This is a system through which one creates one or more pipelines to process clinical notes and to identify clinical named entities. It processes clinical notes, identifying types of clinical named entities, drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures. Each named entity that is found is given attributes for the text span, the ontology mapping code, the context (family history of, current, unrelated to patient), and negated/not negated. cTAKES is built on the UIMA framework. cTAKES 2.5 does not provide a GUI of its own for installation or processing. The cTAKES documentation shows how to use the GUIs provided by the UIMA framework, and how to run cTAKES from a command line. Before using cTAKES you need to know that cTAKES does not provide any mechanisms of its own to handle patient data securely. It is assumed that cTAKES is installed on a system that can process patient data, or that any data being processed by cTAKES has already been through a deidentification step in order to comply with any applicable laws. The tool has been developed and deployed at Mayo Clinic since early 2000.

Proper citation: cTAKES (RRID:SCR_006379) Copy   

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https://wiki.nci.nih.gov/display/LexEVS/LexGrid

LexGrid (Lexical Grid) provides support for a distributed network of lexical resources such as terminologies and ontologies via standards-based tools, storage formats, and access/update mechanisms. The Lexical Grid Vision is for a distributed network of terminological resources. It is the foundation of the National Center for Biomedical Ontology BioPortal interface and web-services, and can parse OBO format, as well as other formats such as OWL. Currently, there are many terminologies and ontologies in existence. Just about every terminology has its own format, its own set of tools, and its own update mechanisms. The only thing that most of these pieces have in common with each other is their incompatibility. This makes it very hard to use these resources to their full potential. We have designed the Lexical Grid as a way to bridge terminologies and ontologies with a common set of tools, formats and update mechanisms. The Lexical Grid is: * accessible through a set of common APIs * joined through shared indices * online accessible * downloadable * loosely coupled * locally extendable * globally revised * available in web-space on web-time * cross-linked The realization of this vision requires three interlocking components, which are: * Standards - access methods and formats need to be published and openly available * Tools - standards based tools must be readily available * Content - commonly used terminologies have to be available for access and download Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: LexGrid (RRID:SCR_006627) Copy   

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http://pid.nci.nih.gov

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 27, 2016. Curated database of information about known biomolecular interactions and key cellular processes assembled into signaling pathways. All interactions are assembled into pathways, and can be accessed by performing searches for biomolecules, or processes, or by viewing predefined pathways. This was a collaborative project between the NCI and Nature Publishing Group (NPG) from 2006 until September 22nd, 2012, and is no longer being updated. PID is aimed at the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists, and immunologists. The database focuses on the biomolecular interactions that are known or believed to take place in human cells. It can be browsed as an online encyclopedia, used to run computational analyses, or employed in ways that combine these two approaches. In addition to PID''''s predefined pathways, search results are displayed as dynamically constructed interaction networks. These features of PID render it a useful tool for both biologists and bioinformaticians. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is supplemented by a concise editorial section that includes specially written synopses of recent important research articles in areas related to cancer research, and specially commissioned Bioinformatics Primers that provide practical advice on how to make the most of other relevant online resources. The database and editorial content are updated monthly, and users can opt to receive a monthly email alert to stay informed about new content. Note: as of September 23, 2012 the PID is no longer being actively curated. NCI will maintain the PID website and data for twelve months beyond September 2012 to allow interested parties to obtain the previously curated data before the site is retired in September 2013.

Proper citation: Pathway Interaction Database (RRID:SCR_006866) Copy   

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http://watson.nci.nih.gov/bioc_mirror/packages/2.11/bioc/html/EDASeq.html

Software for numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: EDASeq (RRID:SCR_006751) Copy   

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http://discover.nci.nih.gov/gominer/GoCommandWebInterface.jsp

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. A web program that organizes lists of genes of interest (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology and automates the analysis of multiple microarrays then integrates the results across all of them in exportable output files and visualizations. High-Throughput GoMiner is an enhancement of GoMiner and is implemented with both a command line interface and a web interface. The program can also: efficiently perform automated batch processing of an arbitrary number of microarrays; produce a human- or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories; integrate the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories; provide a fast form of false discovery rate multiple comparisons calculation; and provide annotations and visualizations for relating transcription factor binding sites to genes and GO categories.

Proper citation: High-Throughput GoMiner (RRID:SCR_000173) Copy   

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https://dctd.cancer.gov/programs/cdp/organization/bbrb

BBRB supports medical discovery and precision medicine by providing leadership, tools, and resources to the biobanking community. Provides input on policy related to biobanking and supports the availability of biospecimens for research. Develops standardized procedures for biospecimen science and research; conducts and sponsors research on the effects of biospecimen preanalytical factors; leads and supports major scientific initiatives requiring high-quality biospecimens; and supports investigations into the ethical, legal, and social issues concerning biospecimen collection and use.

Proper citation: Biorepositories and Biospecimens Research Branch (RRID:SCR_013979) Copy   

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http://www-lecb.ncifcrf.gov/NCISEM97/ncisem18.html

The Flicker image viewer is a Java applet which reads two images from the Internet and then displays them in the your Web browser. It allows you to enhance them in various ways and to compare them visually in a third window called the flicker window. The Open-source Flicker downloadable application is now available. The program uses the "flicker method" used in GELLAB with and with Xconf. The flicker method is the alternate display in the same visual space of two images being compared which are aligned by aligning similar morphologic features. Images may first be enhanced by spatial warping, pseudo 3-Dimensional projections, image sharpening, contrast enhancement and other transforms. The transformed images may then be presented using flickering. Flicker is a method for comparing images from different Internet sources on your Web browser. Scientists around the world often work on similar image data. More of this data is being published on the Internet each year. In the case of 2D protein electrophoretic gel images, maps identifying proteins in these gels are becoming increasingly available. Visually comparing 2D sample gels against these 2D gel database maps may suggest putative protein spot identification in many cases. Flicker was originally developed for comparing 2D protein gels across the Internet.

Proper citation: NCI Flicker Web Server. (RRID:SCR_003390) Copy   

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http://dtp.nci.nih.gov/branches/btb/services.html

National Cancer Institute Biorepositories provides information on the tumor repository, animal production program, and access to other NIH Repositories. A catalog of in vitro cell lines, transplantable animal and human tumors and microarrays, including ordering information, is available. In the Animal Production Program, rodents are supplied to Federal Institutions and to grantees/contractors with a current OLAW Assurance on file. Requests from international entities seeking rodent strains solely available from the NCI Animal Production Program must have an OLAW Assurance or current AAALACi accreditation and will be evaluated on a case-by-case basis. In general, the rodents are supplied to the following: * Researchers at NCI-Frederick * NIH on-campus laboratories * NIH/NCI funded research contracts * NIH/NCI funded grantees * Other government agencies To meet the above needs, animal production contracts plus quality control contracts are used. The animal production contracts are used to propagate the NIH sublines of the strains. Many of the strains produced are not available commercially and the NCI program is the sole source of several lines.

Proper citation: National Cancer Institute Biorepositories (RRID:SCR_004789) Copy   

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http://epi.grants.cancer.gov/CFR/about_colon.html

It is an international research infrastructure for investigators interested in conducting population and clinic-based interdisciplinary studies on the genetic and molecular epidemiology of colon cancer and its behavioral implications. A central goal of the C-CFR is the translation of this research to the clinical and prevention setting for the benefit of Registry participants and the general public. The C-CFR has information and biospecimens contributed by greater than 11,300 families across the spectrum of risk for colon cancers and from population-based or relative controls. Of particular interest are: identification and characterization of cancer susceptibility genes definition of gene-gene and gene-environment interactions in cancer etiology translational, preventive, and behavioral implications of research findings Special features include: population-based and clinic-based ascertainment systematic collection of validated family history epidemiologic risk factor data clinical and follow-up data biospecimens (including tumor blocks and EBV transformed cell lines) ongoing molecular characterization of the participating families Goals: to contribute to the development of public health measures for the general population by increasing knowledge on genetic factors affecting cancer susceptibility and modification by environmental and lifestyle factors to protect those with increased susceptibility from developing cancer to provide life-prolonging treatment to genetically susceptible individuals Objectives: to establish a comprehensive research resource infrastructure to assist with the implementation of collaborative, interdisciplinary research protocols in the genetic epidemiology of cancer to identify, characterize, and follow-up a cohort of individuals and their family members, spanning the spectrum of cancer risk to identify diverse genetically susceptible populations that could benefit from enrollment in preventive and therapeutic interventions to develop an adaptive and evolving informatics model to support ongoing and future research consortia Sponsor. This study was supported by National Cancer Institute Grants R01 CA47147, R01 CA47305, and R01 CA69664.

Proper citation: Colon CFR (RRID:SCR_013162) Copy   

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

The SKY/M-FISH and CGH databases provide a public platform for investigators to share and compare their molecular cytogenetic data. The database is open to everyone and all users can view an individual investigator's public data or compare public cases from different investigators. Those wishing to contribute their own data must register and can choose to keep their data private for a period not to exceed two years. Spectral Karyotyping (SKY), Multiplex Fluorescence In Situ Hybridization (M-FISH) and Comparative Genomic Hybridization (CGH) are complementary fluorescent molecular cytogenetic techniques. SKY/M-FISH permits the simultaneous visualization of each human or mouse chromosome in a different color, facilitating the identification of chromosomal aberrations. CGH utilizes the hybridization of differentially labeled tumor and reference DNA to generate a map of DNA copy number changes in tumor genomes.

Proper citation: SKY/M-FISH/CGH (RRID:SCR_007933) Copy   

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http://dtp.nci.nih.gov/docs/3d_database/dis3d.html

The NCI DIS 3D database is a collection of 3D structures for over 400,000 drugs. The database is an extension of the NCI Drug Information System. The structural information stored in the DIS is only the connection table for each drug. The connection table is just a list of which atoms are connected and how they are connected. It is essentially a searcheable database of three-dimensional structures has been developed from the chemistry database of the NCI Drug Information System (DIS), a file of about 450,000 primarily organic compounds which have been tested by NCI for anticancer activity. The DIS database is very similar in size and content to the proprietary databases used in the pharmaceutical industry; its development began in the 1950s; and this history led to a number of problems in the generation of 3D structures. This information can be searched to find drugs that share similar patterns of connections, which can correlate with similar biological activity. But the cellular targets for drug action, as well as the drugs themselves, are 3 dimensional objects and advances in computer hardware and software have reached the point where they can be represented as such. In many cases the important points of interaction between a drug and its target can be represented by a 3D arrangement of a small number of atoms. Such a group of atoms is called a pharmacophore. The pharmacophore can be used to search 3D databases and drugs that match the pharmacophore could have similar biological activity, but have very different patterns of atomic connections. Having a diverse set of lead compounds increases the chances of finding an active compound with acceptable properties for clinical development. Sponsor: The ICBG are supported by the Cooperative Agreement mechanism, with funds from nine components of the NIH, the National Science Foundation, and the Foreign Agricultural Service of the USDA.

Proper citation: National Cancer Institute 3D Structure Database (RRID:SCR_008211) Copy   

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http://pluto3.nci.nih.gov/tissue/default.cfm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. The Specimen Resource Locator is a database to help researchers locate human specimens (tissue, serum, DNA/RNA, other specimens) for cancer research. It includes tissue banks and tissue procurement systems with access to normal, benign, precancerous and cancerous human tissue from a variety of organs. Researchers specify the types of specimens, number of cases, preservation methods and associated data they require. The Locator will then search the database and return a list of tissue resources most likely to meet their requirements. When no match is obtained, the researcher is referred to the NCI Tissue Expediter ([email protected]). The Tissue expediter is a scientist who can help researchers identify appropriate resources and/or appropriate collaborators.

Proper citation: NCI Specimen Resource Locator (RRID:SCR_004754) Copy   

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http://dctd.cancer.gov/

Division of NCI that takes prospective cancer detection and treatment leads, facilitates their paths to clinical application, and expedites the initial and subsequent large-scale testing of new agents, biomarkers, imaging tests, and other therapeutic interventions (radiation, surgery, immunotherapy) in patients. DCTD, like all of NCI, supports many programs that could not be done without government funding - investigators supported by the division engage in scientifically sound, high-risk research that may yield great benefits for patients with cancer, but are too difficult or risky for industry or academia to pursue. This includes a particular emphasis on the development of distinct molecular signatures for cancer, refined molecular assays, and state-of-the-art imaging techniques that will guide oncologic therapy in the future. The division has eight major programs that work together to bring unique molecules, diagnostic tests, and therapeutic interventions from the laboratory bench to the patient bedside: * Cancer Diagnosis Program * Cancer Imaging Program * Cancer Therapy Evaluation Program * Developmental Therapeutics Program * Radiation Research Program * Translational Research Program * Biometrics Research Branch * Office of Cancer Complementary and Alternative Medicine

Proper citation: DCTD (RRID:SCR_004196) Copy   

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http://discover.nci.nih.gov/gominer/

GoMiner is a tool for biological interpretation of "omic" data including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question, What does it all mean biologically? To answer this question, GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories. The insights gained through GoMiner can generate hypotheses to guide additional research. GoMiner displays the genes within the framework of the Gene Ontology hierarchy in two ways: * In the form of a tree, similar to that in AmiGO * In the form of a "Directed Acyclic Graph" (DAG) The program also provides: * Quantitative and statistical analysis * Seamless integration with important public databases GoMiner uses the databases provided by the GO Consortium. These databases combine information from a number of different consortium participants, include information from many different organisms and data sources, and are referenced using a variety of different gene product identification approaches.

Proper citation: GoMiner (RRID:SCR_002360) Copy   

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http://mpr.nci.nih.gov/prow/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. It offers short, structured reviews of proteins and protein families, especially leukocyte surface membrane molecules. Index of information available from PROW includes CD molecule, Alternate names, Current Guides, Past Guides, Entrez Gene and Assigning workshop. Current guides: expanded format including Summary Sentence and Abstract Past guides: older guides with excellent information, some data may be dated

Proper citation: PROW (RRID:SCR_002434) Copy   

•   Source: SciCrunch Registry