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http://seer.cancer.gov/

SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.

Proper citation: Surveillance Epidemiology and End Results (RRID:SCR_006902) Copy   

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http://www.zbh.uni-hamburg.de/?id=292

A web-based software tool for the integrative analysis of cancer genomics data. It stores different kinds of downstream processed data from multiple samples in a single database. A powerful search interface allows to dynamically filter the data to be displayed with respect to different criteria. The combination of AJAX technology and a fast visualization engine facilitates a highly dynamic visualization for large amounts of data. FISH Oracle 2 is able to simultaneously display different data sets, thus simplifying their comparison. Filter and display options can be changed on the fly. High quality image export enables the life scientist to easily communicate the results, e.g. in presentations or publications. A comprehensive data administration assures to keep track of the data stored in the database.

Proper citation: FISH Oracle (RRID:SCR_010927) Copy   

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https://omictools.com/l2l-tool

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on August 26, 2019.

Database of published microarray gene expression data, and a software tool for comparing that published data to a user''''s own microarray results. It is very simple to use - all you need is a web browser and a list of the probes that went up or down in your experiment. If you find L2L useful please consider contributing your published data to the L2L Microarray Database in the form of list files. L2L finds true biological patterns in gene expression data by systematically comparing your own list of genes to lists of genes that have been experimentally determined to be co-expressed in response to a particular stimulus - in other words, published lists of microarray results. The patterns it finds can point to the underlying disease process or affected molecular function that actually generated the observed changed in gene expression. Its insights are far more systematic than critical gene analyses, and more biologically relevant than pure Gene Ontology-based analyses. The publications included in the L2L MDB initially reflected topics thought to be related to Cockayne syndrome: aging, cancer, and DNA damage. Since then, the scope of the publications included has expanded considerably, to include chromatin structure, immune and inflammatory mediators, the hypoxic response, adipogenesis, growth factors, hormones, cell cycle regulators, and others. Despite the parochial origins of the database, the wide range of topics covered will make L2L of general interest to any investigator using microarrays to study human biology. In addition to the L2L Microarray Database, L2L contains three sets of lists derived from Gene Ontology categories: Biological Process, Cellular Component, and Molecular Function. As with the L2L MDB, each GO sub-category is represented by a text file that contains annotation information and a list of the HUGO symbols of the genes assigned to that sub-category or any of its descendants. You don''''t need to download L2L to use it to analyze your microarray data. There is an easy-to-use web-based analysis tool, and you have the option of downloading your results so you can view them at any time on your own computer, using any web browser. However, if you prefer, the entire L2L project, and all of its components, can be downloaded from the download page. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: L2L Microarray Analysis Tool (RRID:SCR_013440) Copy   

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http://www.genesigdb.org

Database of traceable, standardized, annotated gene signatures which have been manually curated from publications that are indexed in PubMed. The Advanced Gene Search will perform a One-tailed Fisher Exact Test (which is equivalent to Hypergeometric Distribution) to test if your gene list is over-represented in any gene signature in GeneSigDB. Gene expression studies typically result in a list of genes (gene signature) which reflect the many biological pathways that are concurrently active. We have created a Gene Signature Data Base (GeneSigDB) of published gene expression signatures or gene sets which we have manually extracted from published literature. GeneSigDB was creating following a thorough search of PubMed using defined set of cancer gene signature search terms. We would be delighted to accept or update your gene signature. Please fill out the form as best you can. We will contact you when we get it and will be happy to work with you to ensure we accurately report your signature. GeneSigDB is capable of providing its functionality through a Java RESTful web service.

Proper citation: GeneSigDB (RRID:SCR_013275) Copy   

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http://www.stanford.edu/group/nusselab/cgi-bin/wnt/

A resource for members of the Wnt community, providing information on progress in the field, maps on signaling pathways, and methods. The page on reagents lists many resources generously made available to and by the Wnt community. Wnt signaling is discussed in many reviews and in a recent book. There are usually several Wnt meetings per year.

Proper citation: Wnt homepage (RRID:SCR_000662) Copy   

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http://bsec.ornl.gov/AdaptiveCrawler.shtml

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 9,2022. A web crawler that can intelligently acquire social media content on the Internet to meet the specific online data source acquisition needs of cancer researchers.

Proper citation: AdaptiveCrawler (RRID:SCR_000573) Copy   

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http://www.cancergenomics.org/

Consortium promoting communication and collaboration among cancer cytogenomics laboratories, who are interested in applying microarray technologies to cancer diagnosis and cancer research. Their oals are to (1) establish platform-neutral and cancer specific microarray designs for diagnostic purposes, (2) share cancer microarray data between participating institutions for education purposes, (3) create a public cancer array database, and (4) carry out multicenter cancer genome translational research. Collaboration amongst the different laboratories and researchers will not only provide validation for the microarray design(s) but ultimately provide more comprehensive molecular information and more accurate interpretation to better serve cancer patients and further cancer research. The CGC was officially incorporated in June 2010 as a not-for-profit organization.

Proper citation: Cancer Genomics Consortium (RRID:SCR_002384) Copy   

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http://hardinmd.lib.uiowa.edu/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2, 2025. A medical database with lists, or directories, of information in health and medicine and images of medical conditions. Users may search Hardin MD, browse through the Medical picture gallery, and sort search results by disease or alphabetical letter.

Proper citation: Hardin MD (RRID:SCR_002364) Copy   

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https://cghub.ucsc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. A secure repository for storing, cataloging, and accessing cancer genome sequences, alignments, and mutation information from the Cancer Genome Atlas (TCGA) consortium and related projects. CGHub gives scientific researchers the statistical power of large cancer genome datasets to attack the molecular complexity of cancer.

Proper citation: Cancer Genomics Hub (RRID:SCR_002657) Copy   

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http://sharedresources.fredhutch.org/core-facilities/bioinformatics

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on July 27,2022. Core provides bioinformatics specialists available to assist researchers with processing, exploring, and understanding genomics data.

Proper citation: Fred Hutchinson Cancer Research Center Co-operative Center for Excellence in Hematology Bioinformatics Resource (RRID:SCR_015324) Copy   

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http://www.civm.duhs.duke.edu/

Biomedical technology research center dedicated to the development of novel imaging methods for the basic scientist and the application of the methods to important biomedical questions. The CIVM has played a major role in the development of magnetic resonance microscopy with specialized MR imaging systems capable of imaging at more than 500,000x higher resolution than is common in the clinical domain. The CIVM was the first to demonstrate MR images using hyperpolarized 3He which has been moved from mouse to man with recent clinical trials performed at Duke in collaboration with GE. More recently the CIVM has developed the molecular imaging workbench---a system dedicated to multimodality cardiopulmonary imaging in the rodent. Their collaborators are employing these unique imaging systems in an extraordinary range of mouse and rat models of neurologic disease, cardiopulmonary disease and cancer to illuminate the underlying biology and explore new therapies.

Proper citation: Center for In Vivo Microscopy (RRID:SCR_001426) Copy   

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https://github.com/Illumina/strelka/

Software for somatic single nucleotide variant (SNV) and small indel detection from sequencing data of matched tumor-normal samples. Strelka2 germline and somatic small variant caller.

Proper citation: Strelka2 (RRID:SCR_005109) Copy   

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http://ranchobiosciences.com/gse4922/

Curated data set of a study that investigated the expression profiles of 347 primary invasive breast tumors on Affymetrix microarrays. Three separate breast cancer cohorts were analyzed: 1) Uppsala (n=249), 2) Stockholm (n=58), 3) Singapore (n=40). The Uppsala and Singapore data can be accessed in GSE4922. The Stockholm cohort data can be accessed at GEO Series GSE1456.

Proper citation: GSE4922 (RRID:SCR_003557) Copy   

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http://www.yale.edu/herzongroup/Herzon_Lab/Home.html

My laboratory has created a family of natural product-inspired anticancer agents. We have evaluated our compounds at the Yale Center for Chemical Genomics, and they are exhibiting IC50 values in the low nM range against K562, HeLa, LnCAP, and HCT-116 lines. Their mechanism of action is unknown, although the natural products have been shown to cleave DNA. An evaluation of the natural products at the NCI has shown that they have a toxicity profile that is distinct from other known DNA damaging agents. We can readily access gram-quantities of these agents for further studies. We are looking for researchers who might find these compounds useful in medicinal applications, for example, for treatment of a specific cancer. We are capable of synthesizing new analogs, such as those incorporating a specific recognition or targeting element, and would be excited to pursue this avenue of research.

Proper citation: Herzon Lab (RRID:SCR_008850) 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://www.cancerdiagnosis.nci.nih.gov/

National program to improve the diagnosis and assessment of cancer by moving scientific knowledge into clinical practice by coordinating and funding resources and research for the development of innovative in vitro diagnostics, novel diagnostic technologies and appropriate human specimens. The Cancer Diagnosis Program is divided into four branches: Biorepository and Biospecimen Research Branch (BBRB), Diagnostic Biomarkers and Technology Branch (DBTB), Diagnostics Evaluation Branch (DEB), and the Pathology Investigation and Resources Branch (PIRB).

Proper citation: CDP (RRID:SCR_004236) Copy   

•   Source: SciCrunch Registry

https://www.bigtencrc.org/

A consortium that aims to transform cancer research through collaborative oncology trials that leverage the scientific and clinical expertise of the Big Ten universities. The goal is to align the conduct of cancer research through collaborative, hypothesis-driven, highly translational oncology trials that leverage the scientific and clinical expertise. The clinical trials that will be developed will be linked to molecular diagnostics, enabling researchers to understand what drives the cancers to grow and what might be done to stop them from growing. The consortium also leverages geographical locations and existing relationships among the cancer centers. One of the consortium's goals is to harmonize contracts and scientific review processes to expedite clinical trials. The consortium will only focus on phase 0 to II trials because larger trials - even a randomized phase II trial - are difficult to conduct at a single cancer center.

Proper citation: Big Ten Cancer Research Consortium (RRID:SCR_004025) Copy   

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http://danstem.ku.dk/

Center consisting of 9 research groups who all address basic questions in stem cell and developmental biology with the overall aim of developing new stem cell-based therapeutic approaches for diabetes and cancer. DanStem comprises two sections: * The Novo Nordisk Foundation Section for Basic Stem Cell Biology (BasicStem) * The Section for Strategic Translational Stem Cell Research and Therapy (TransStem) DanStem was established as a result of a series of international recruitments coupled with internationally recognized research groups focused on insulin producing beta cells and cancer research already located at the University of Copenhagen. They all have well-established, international collaborations and actively participate in several international scientific consortia. DanStem is also active in training undergraduates, PhD students and postdocs.

Proper citation: DanStem (RRID:SCR_004021) Copy   

•   Source: SciCrunch Registry