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https://datacommons.cancer.gov

Cloud based data science infrastructure that provides secure access to cancer research data from NCI programs and key external cancer programs. Serves as coordinated resource for public data sharing of NCI funded programs. Users can explore and use analytical and visualization tools for data analysis. Enables to search and aggregate data across repositories including Cancer Data Service, Clinical Trial Data Commons, Genomic Data Commons, Imaging Data Commons, Integrated Canine Data Commons, Proteomic Data Commons.

Proper citation: Cancer Research Data Commons (RRID:SCR_019128) Copy   

•   Source: SciCrunch Registry

http://www.wikicancer.org/

A place where people connected to cancer can share real-life experiences -- fears, insights, stories, and advice. Adding perspectives is easy, and every contribution builds the site into a more valuable and unique community resource. Content, resources, and support on wikiCancer: * Just been diagnosed with cancer? * Living with cancer * For cancer survivors * How to support someone with cancer * Connect with other cancer patients, survivors, family and caregivers

Proper citation: wikiCancer (RRID:SCR_001824) Copy   

•   Source: SciCrunch Registry

https://www.urmc.rochester.edu/neurosurgery/specialties/neurooncology.aspx

Collaborative neuro-oncology research program with a tissue repository (tumor bank) containing a wide range of clinical specimens, which they make available to researchers in order to study the effects of new drugs on a large number and wide range of tumor specimens. They provide highly coordinated, complex care in neurosurgery, radiation oncology, medical oncology, and neurology to patients afflicted with tumors of the brain and spine by combining the newest technologies and treatments available anywhere in the world. The program is formed from a multidisciplinary group with a goal of helping patients navigate the complex issues surrounding brain and spinal cancer care. The researchers are working to increase the number of targets that could be considered for anti-angiogenesis therapy. Many of their studies focus on the blood vessel cells (endothelial cells) themselves, which, unlike tumor cells, rarely mutate and so might be less likely to become resistant to therapy and are also more easily reached through the bloodstream. Their researchers are also attempting to better understand the changes in the blood-brain barrier (BBB) that are associated with fluid accumulation and brain swelling (edema) in neuro-oncology patients. Normal brain tissue is shielded from the rest of the body by the BBB. This barrier is composed of very tight blood vessels that prevent most substances from entering the brain. Brain tumors have a leaky BBB ����?? this feature can be used to identify tumors on MRI scans. They have identified specific molecules that appear to be associated with the leaky, abnormal vessels while the normal blood vessels with intact BBB produce these molecules at very low levels or not at all. Inhibiting the function of these molecules may help control or prevent disruption of the BBB and limit cerebral edema in brain tumor patients, as well as patients suffering from stroke or traumatic brain injury.

Proper citation: University of Rochester Program for Brain Tumors and Spinal Tumors (RRID:SCR_005343) Copy   

•   Source: SciCrunch Registry

http://cancer.ucsf.edu/research/cores/biostatistics

The Biostatistics Core provides statistical support for cancer-related research at UCSF, focusing particulary on applications in clinical trials and population studies. The Computational Biology Core supports applications to genomics, genetics and molecular biology. Core faculty have expertise in study design, protocol and proposal development and review, data analysis, and publication of results. Support for Cancer Center investigators participating in established Site Committees is typically handled by the faculty member assigned to that committee. Other requests can be directed to the consulting service request page maintained by the UCSF Clinical & Translational Science Institute (CTSI). These requests will then be assigned to a Core faculty member. Basic consulting services are generally provided free of charge to Cancer Center Members. Members requiring frequent assistance are encouraged to provide regular salary support to a Core statistician when possible to support more extensive requests and for long-term projects. Services: * Study Design * Guidance on Study Conduct * Data Analysis and Reporting of Study Results * Teaching resources

Proper citation: UCSF Helen Diller Family Comprehensive Cancer Center Biostatistics Core (RRID:SCR_005701) Copy   

•   Source: SciCrunch Registry

http://ucsd.researchaccelerator.org/

Software platform that allows researchers to easily collaborate on research and share reagents, antibodies, cell lines and more. It is designed to increase scientific collaboration across disciplines and geographical boundaries. Among the institutions now using the platform include Yale University, U of Pennsylvania, U of Chicago, Washington U, Cambridge University, University College London. The platform is licensed to select institutions. ResearchAccelerator.org allows researchers to form targeted, data driven collaborations. Researchers can search for data based on gene, disease and pathway, and they can post data which would otherwise be orphaned. The resulting collaborations, which are likely to be transdisciplinary, can greatly amplify impact and research productivity.

Proper citation: Research Accelerator (RRID:SCR_006051) Copy   

•   Source: SciCrunch Registry

http://lincs.hms.harvard.edu/db/

Database that contains all publicly available HMS LINCS datasets and information for each dataset about experimental reagents and experimental and data analysis protocols. Experimental reagents include small molecule perturbagens, cells, antibodies, and proteins.

Proper citation: HMS LINCS Database (RRID:SCR_006454) Copy   

•   Source: SciCrunch Registry

http://wiki.chasmsoftware.org/index.php/Main_Page

CHASM is a method that predicts the functional significance of somatic missense mutations observed in the genomes of cancer cells, allowing mutations to be prioritized in subsequent functional studies, based on the probability that they give the cells a selective survival advantage. SNV-Box is a database of pre-computed features of all possible amino acid substitutions at every position of the annotated human exome. Users can rapidly retrieve features for a given protein amino acid substitution for use in machine learning.

Proper citation: CHASM/SNV-Box (RRID:SCR_006445) Copy   

•   Source: SciCrunch Registry

http://dgidb.genome.wustl.edu/

A database of drug-gene relationships that provides drug-gene interactions and potential druggability data given list of genes. There are about 15 data sources that are being aggregated by DGIdb, with update date and these data sources are listed on this page: http://dgidb.genome.wustl.edu/sources, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DGIdb (RRID:SCR_006608) Copy   

•   Source: SciCrunch Registry

http://p53.fr

The UMD TP53 Mutation Database is a novel web site exclusively dedicated to mutant TP53. The following datasets, analytical tools and software are available. * The TP53 UMD mutation database in human cancer (2012 release). This novel release (35,000 mutations, 3,600 publications) has been highly curated using an original and novel statistical procedure (See Edlung et al. PNAS 2012). * TP53MUTLOAD (MUTant Loss Of Activity Database), a novel database dedicated to detailed analysis of the properties of each TP53 mutant, ranging from transactivation to cell growth properties, change of conformation, localization or various gains of functions. The database contains more than 110,000 different entries. * TP53 Mut assessor, a novel stand-alone software available for both Windows and Mac users. Check your favorite TP53 mutants and get an instant identity card. Very useful to analyze any newly discovered TP53 mutants, as the software checks for every possible TP53 mutation. * MUT-TP53 2.0, an accurate and powerful tool that automatically manages p53 mutations and generate tables ready for publication, decreasing the risk of typing errors. MUT-TP53 2.0 also provides specific information for each TP53 mutation, allowing the user to assess the quality of the data. Up to 500 TP53 mutations can be managed simultaneously.

Proper citation: UMD p53 Mutation Database (RRID:SCR_006720) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/CANCO

A vocabulary that is able to describe and semantically interconnect the different paradigms of the cancer chemoprevention domain.

Proper citation: Cancer Chemoprevention Ontology (RRID:SCR_006966) Copy   

•   Source: SciCrunch Registry

http://cancer.ucsf.edu/

The UCSF Helen Diller Family Comprehensive Cancer Center combines basic science, clinical research, epidemiology/cancer control, and patient care throughout the University of California, San Francisco. UCSF''s long tradition of excellence in cancer research includes, notably, the Nobel Prize-winning work of J. Michael Bishop and Harold Varmus, who discovered cancer-causing oncogenes. Their work opened new doors for exploring genetic mistakes that cause cancer, and formed the basis for some of the most important cancer research happening today. * Basic Scientific Research: From understanding normal cellular processes and replication to discovering the underlying molecular and genetic causes of cancer when these processes go awry, UCSF researchers are committed to moving scientific insights beyond model systems and pursuing their relevance for clinical oncology and cancer prevention. * Clinical Research: Clinical scientists explore how greater understanding of fundamental biological events can be transformed into clinically relevant tools. New forms of cancer treatment, as well as innovations in diagnosis and prognosis, undergo rigorous evaluation for safety and efficacytranslating into improved patient outcomes and hope for the future. * Patient Care: The Helen Diller Family Comprehensive Cancer Center provides superlative cancer patient care at four San Francisco medical centers: UCSF Medical Center at Mount Zion; UCSF Medical Center at Parnassus; San Francisco General Hospital; and the San Francisco Veterans Affairs Medical Center. * Population Science: Cancer population sciences at UCSF includes a broad range of research on the causes of new cancers and the sickness and death due to the disease in order to develop ways to improve the prevention and early detection of cancer as well as the quality of life following diagnosis and treatment for all of Northern California''s diverse populations.

Proper citation: UCSF Helen Diller Family Comprehensive Cancer Center (RRID:SCR_008857) Copy   

•   Source: SciCrunch Registry

http://www.cancerimagingarchive.net/

Archive of medical images of cancer accessible for public download. All images are stored in DICOM file format and organized as Collections, typically patients related by common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. Neuroimaging data sets include clinical outcomes, pathology, and genomics in addition to DICOM images. Submitting Data Proposals are welcomed.

Proper citation: Cancer Imaging Archive (TCIA) (RRID:SCR_008927) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/NPO

An ontology that represents the basic knowledge of physical, chemical and functional characteristics of nanotechnology as used in cancer diagnosis and therapy.

Proper citation: NanoParticle Ontology (RRID:SCR_010369) Copy   

•   Source: SciCrunch Registry

http://bg.upf.edu/transfic/home

A method to transform Functional Impact scores taking into account the differences in basal tolerance to germline SNVs of genes that belong to different functional classes.

Proper citation: TransFIC (RRID:SCR_010788) Copy   

•   Source: SciCrunch Registry

http://genomics.senescence.info/

Collection of databases and tools designed to help researchers study the genetics of human ageing using modern approaches such as functional genomics, network analyses, systems biology and evolutionary analyses. A major resource in HAGR is GenAge, which includes a curated database of genes related to human aging and a database of ageing- and longevity-associated genes in model organisms. Another major database in HAGR is AnAge. Featuring over 4,000 species, AnAge provides a compilation of data on aging, longevity, and life history that is ideal for the comparative biology of aging. GenDR is a database of genes associated with dietary restriction based on genetic manipulation experiments and gene expression profiling. Other projects include evolutionary studies, genome sequencing, cancer genomics, and gene expression analyses. The latter allowed them to identify a set of genes commonly altered during mammalian aging which represents a conserved molecular signature of aging. Software, namely in the form of scripts for Perl and SPSS, is made available for users to perform a variety of bioinformatic analyses potentially relevant for studying aging. The Perl toolkit, entitled the Ageing Research Computational Tools (ARCT), provides modules for parsing files, data-mining, searching and downloading data from the Internet, etc. Also available is an SPSS script that can be used to determine the demographic rate of aging for a given population. An extensive list of links regarding computational biology, genomics, gerontology, and comparative biology is also available.

Proper citation: Human Ageing Genomic Resources (RRID:SCR_007700) Copy   

•   Source: SciCrunch Registry

https://dcc.icgc.org/

Portal provides tools for visualizing, querying, and downloading cancer data, which is released on quarterly schedule.

Proper citation: ICGC Data Portal (RRID:SCR_021722) Copy   

•   Source: SciCrunch Registry

http://cancer.gov/cancertopics/pdq/cancerdatabase

NCI''s comprehensive cancer database that contains summaries on a wide range of cancer topics; a registry of 8,000+ open and 19,000+ closed cancer clinical trials from around the world; a directory of professionals who provide genetics services; the NCI Dictionary of Cancer Terms, with definitions for 6,800+ cancer and medical terms; and the NCI Drug Dictionary, which has information on 2,300+ agents used in the treatment of cancer or cancer-related conditions. The PDQ cancer information summaries are peer reviewed and updated monthly by six editorial boards comprised of specialists in adult treatment, pediatric treatment, supportive care, screening and prevention, genetics, and complementary and alternative medicine. The Boards review current literature from more than 70 biomedical journals, evaluate its relevance, and synthesize it into clear summaries. Many of the summaries are also available in Spanish.

Proper citation: Physician Data Query (RRID:SCR_006833) Copy   

•   Source: SciCrunch Registry

https://www.synapse.org/

A cloud-based collaborative platform which co-locates data, code, and computing resources for analyzing genome-scale data and seamlessly integrates these services allowing scientists to share and analyze data together. Synapse consists of a web portal integrated with the R/Bioconductor statistical package and will be integrated with additional tools. The web portal is organized around the concept of a Project which is an environment where you can interact, share data, and analysis methods with a specific group of users or broadly across open collaborations. Projects provide an organizational structure to interact with data, code and analyses, and to track data provenance. A project can be created by anyone with a Synapse account and can be shared among all Synapse users or restricted to a specific team. Public data projects include the Synapse Commons Repository (SCR) (syn150935) and the metaGenomics project (syn275039). The SCR provides access to raw data and phenotypic information for publicly available genomic data sets, such as GEO and TCGA. The metaGenomics project provides standardized preprocessed data and precomputed analysis of the public SCR data.

Proper citation: Synapse (RRID:SCR_006307) Copy   

•   Source: SciCrunch Registry

http://www.cta.lncc.br/

A database of information about each Cancer-Testis (CT) gene, its gene products and the immune response induced in cancer patients by these proteins. CT antigens are proteins normally expressed only in the human germ line but that are also present in a significant subset of malignant tumors. The practical importance of these proteins is that due to their restricted expression pattern they are frequently recognized by the immune system of cancer patients. Moreover, this antigenicity has raised the possibility of their being used as vaccines to actively stimulate immune responses in order to combat tumor growth. As a result worldwide research into many aspects of CT antigens is rapidly growing prompting the construction of this database as a resource for investigators involved in this area.

Proper citation: CTDatabase (RRID:SCR_007614) Copy   

•   Source: SciCrunch Registry

http://ercsb.ewha.ac.kr:8080/FusionGene/

Knowledgebase of fusion transcripts collected from various public resources such as the Sanger CGP, OMIM, PubMed, and Mitelman's database. It is an alignment viewer to facilitate examining reliability of fusion transcripts and inferring functional significance., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ChimerDB (RRID:SCR_007596) Copy   

•   Source: SciCrunch Registry