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https://research.kssg.ch/Projekte/nach-Projekttyp/48182/L-en

St. Gallen Lung Biopsy Biobank is a tissue and blood sample collation (biobank) of all patients willing to give informed consent, who are undergoing bronchoscopy and/or surgical lung resection in their diagnostic pathway for any pulmonary disease. The special feature of the proposed biobank is the collection of endobronchial biopsies, blood, and molecularly defined cell populations from tumor tissue in a quality suitable for mRNA and other protein expression techniques, which is not possible by standard biobanks.

Proper citation: St. Gallen Lung Biopsy Biobank (RRID:SCR_004971) Copy   

•   Source: SciCrunch Registry
  

http://frederick.cancer.gov/

A federally funded research and development center dedicated to biomedical research. NCI-Frederick partners with university, government, and corporate scientists to speed the translation of laboratory research into new diagnostic tests and treatments for cancer and HIV/AIDS. NCI-Frederick is comprised of more than 2,800 government- and contractor-employed biomedical researchers, laboratory technicians, and support staff and several cancer research centers. The FNLCR provides quick response capabilities and meets special long-term research and development needs for NCI that cannot be met as effectively by existing in-house or contractor resources.

Proper citation: NCI-Frederick (RRID:SCR_004880) Copy   

•   Source: SciCrunch Registry
  

http://www.utmb.edu/scccb/htbc/htbc.htm

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016.

Proper citation: University of Texas Tumor Bank (RRID:SCR_005048) Copy   

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http://www.broadinstitute.org/cancer/cga/oncotator

A tool for annotating human genomic point mutations and indels with data relevant to cancer researchers. Genomic Annotations, Protein Annotations, and Cancer Annotations are aggregated from many resources. A standalone version of Oncotator is being developed.

Proper citation: Oncotator (RRID:SCR_005183) Copy   

•   Source: SciCrunch Registry
  

http://research-public.gene.com/Research/genentech/canpredict/index.html

Web application that uses a combination of computational methods to identify those changes most likely to be cancer-associated.

Proper citation: CanPredict (RRID:SCR_008216) Copy   

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http://clinicalinformatics.stanford.edu/services/biobank.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 7th, 2023. An online, searchable record of biospecimen attributes and storage location for the following three biobanks at Stanford: * The Bone Marrow Transplant (BMT) program * The Hematology biospecimen bank is a research sample repository focusing on blood, plasma and bone marrow, primarily from leukemia patients. * Cancer Center Pathology Core: The Stanford Comprehensive Cancer Center Tissue Bank stores research samples of both solid tumor and blood from cancer patients, with an emphasis on prostate. As of November 2009, the system contains data on 50,000 biospecimens stored in multiple banks at Stanford. An anonymous Biospecimen Locator allows Stanford researchers to search the STRIDE Virtual Biospecimen Bank for suitable samples without having to know, or expose, any protected patient-specific information. Having determined that suitable specimens exist in one of the constituent biospecimens banks, the researcher can generate a Web-based request form to obtain additional information from the appropriate bank operator(s). Access to specimens is restricted to Stanford Faculty and Staff or affiliates., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: STRIDE Virtual Biospecimen Bank (RRID:SCR_008667) Copy   

•   Source: SciCrunch Registry
  

http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000674.v1.p1

Human genetics data from an immense (78,000) and ethnically diverse population available for secondary analysis to qualified researchers through the database of Genotypes and Phenotypes (dbGaP). It offers the opportunity to identify potential genetic risks and influences on a broad range of health conditions, particularly those related to aging. The GERA cohort is part of the Research Program on Genes, Environment, and Health (RPGEH), which includes more than 430,000 adult members of the Kaiser Permanente Northern California system. Data from this larger cohort include electronic medical records, behavioral and demographic information from surveys, and saliva samples from 200,000 participants obtained with informed consent for genomic and other analyses. The RPGEH database was made possible largely through early support from the Robert Wood Johnson Foundation to accelerate such health research. The genetic information in the GERA cohort translates into more than 55 billion bits of genetic data. Using newly developed techniques, the researchers conducted genome-wide scans to rapidly identify single nucleotide polymorphisms (SNPs) in the genomes of the people in the GERA cohort. These data will form the basis of genome-wide association studies (GWAS) that can look at hundreds of thousands to millions of SNPs at the same time. The RPGEH then combined the genetic data with information derived from Kaiser Permanente''s comprehensive longitudinal electronic medical records, as well as extensive survey data on participants'' health habits and backgrounds, providing researchers with an unparalleled research resource. As information is added to the Kaiser-UCSF database, the dbGaP database will also be updated.

Proper citation: Resource for Genetic Epidemiology Research on Adult Health and Aging (RRID:SCR_010472) Copy   

•   Source: SciCrunch Registry
  

http://methycancer.psych.ac.cn/

Database to study interplay of DNA methylation, gene expression and cancer that hosts both highly integrated data of DNA methylation, cancer-related gene, mutation and cancer information from public resources, and the CpG Island (CGI) clones derived from our large-scale sequencing. Interconnections between different data types were analyzed and presented. Search tool and graphical MethyView are developed to help users access all the data and data connections and view DNA methylation in context of genomics and genetics data. The search tool and graphical MethyView are developed to help users access all the data and data connections and view DNA methylation in context of genomics and genetics data. As part of the Cancer Epigenomics Project in China, MethyCancer serves as a platform for sharing data and analytical results from the Cancer Genome/Epigenome Project in China with colleagues all over the world.

Proper citation: MethyCancer (RRID:SCR_013399) Copy   

•   Source: SciCrunch Registry
  

https://portal.gdc.cancer.gov/

A unified data repository of the National Cancer Institute (NCI)'s Genomic Data Commons (GDC) that enables data sharing across cancer genomic studies in support of precision medicine. The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI). The GDC Data Portal provides a platform for efficiently querying and downloading high quality and complete data. The GDC also provides a GDC Data Transfer Tool and a GDC API for programmatic access.

Proper citation: Genomic Data Commons Data Portal (GDC Data Portal) (RRID:SCR_014514) Copy   

•   Source: SciCrunch Registry
  

http://cancer3d.org

Database that allows for the exploration of cancer on somatic missense mutations from the Cancer Genome Atlas and Cancer Cell Line Encyclopedia. The site maps proteins and mutations using 3D models and is an interface to the algorithms e-Driver and e-Drug allowing for the prediction of novel cancer drivers or drug biomarkers.

Proper citation: Cancer3D (RRID:SCR_013755) Copy   

•   Source: SciCrunch Registry
  

https://ckb.jax.org/

Semi-automated and manually curated database of gene/variant annotations, therapy knowledge, diagnostic/prognostic information, and oncology clinical trials. Users can search CKB via gene, gene variants, drug, drug class, indication, and clinical trials.

Proper citation: Jackson Laboratory Clinical Knowledgebase (RRID:SCR_014965) Copy   

•   Source: SciCrunch Registry
  

https://www.jax.org/jax-mice-and-services/in-vivo-pharmacology/mouse-tumor-biology-database

Database supports use of mouse model system for human cancer by providing comprehensive resource for data and information on various tumor models.

Proper citation: Mouse Tumor Biology Database (RRID:SCR_006517) Copy   

•   Source: SciCrunch Registry
  

http://www.iiserpune.ac.in/~coee/histome/

Database of human histone variants, sites of their post-translational modifications and various histone modifying enzymes. The database covers 5 types of histones, 8 types of their post-translational modifications and 13 classes of modifying enzymes. Many data fields are hyperlinked to other databases (e.g. UnprotKB/Swiss-Prot, HGNC, OMIM, Unigene etc.). Additionally, this database also provides sequences of promoter regions (-700 TSS +300) for all gene entries. These sequences were extracted from the UCSC genome browser. Sites of post-translational modifications of histones were manually searched from PubMed listed literature. Current version contains information for about ~50 histone proteins and ~150 histone modifying enzymes. HIstome is a combined effort of researchers from two institutions, Advanced Center for Treatment, Research and Education in Cancer (ACTREC), Navi Mumbai and Center of Excellence in Epigenetics (CoEE), Indian Institute of Science Education and Research (IISER), Pune.

Proper citation: HIstome: The Histone Infobase (RRID:SCR_006972) Copy   

•   Source: SciCrunch Registry
  

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

The Breast Cancer Family Registry (Breast CFR) and the Colon Cancer Family Registry (Colon CFR) were established by the National Cancer Institute (NCI) as a unique resource for investigators to use in conducting studies on the genetics and molecular epidemiology of breast and colon cancer. Known collectively as the CFRs, they share a central goal: the translation of research to the clinical and prevention settings for the benefit of Registry participants and the general public. The CFRs are particularly interested in: * Identifying and characterizing cancer susceptibility genes; * Defining gene-gene and gene-environment interactions in cancer etiology; and * Exploring the translational, preventive, and behavioral implications of research findings. The CFRs do not provide funding for studies; however, researchers can apply to access CFR data and biospecimens contributed by thousands of families from across the spectrum of risk for these cancers and from population-based or relative controls. Special features of the CFRs include: * Population-based and clinic-based ascertainment; * Systematic collection of validated family history; * Epidemiologic risk factor , clinical, and followup data; * Biospecimens (including tumor blocks and Epstein-Barr virus (EBV)-transformed cell lines); * Ongoing molecular characterization of the participating families; and * A combined informatics center.

Proper citation: NCI Breast and Colon Cancer Family Registries (RRID:SCR_006664) 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   

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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://driverdb.ym.edu.tw/DriverDB/intranet/init.do

A database for cancer driver gene/mutation that incorporates a huge amount of exome-seq data, annotation databases (such as dbSNP, 1000 Genome and Cosmic), and published bioinformatics algorithms dedicated to driver gene/mutation identification.

Proper citation: DriverDB (RRID:SCR_007736) Copy   

•   Source: SciCrunch Registry
  

http://cccb.dfci.harvard.edu

Core facility that provides the following services: Microarray and other genomic data analysis, MiSeq. The Center provides broad-based support for the generation, analysis, and interpretation of genomic and other large-scale data in the context of basic, clinical and translational research. The CCCB has three primary elements. * The CCCB sequencing facility offers a wide range of services to assist in the design and execution of next-generation sequencing projects. Utilizing the Illumina (Solexa) sequencing technology, they currently support a number of applications inlcuding ChIP-Seq, RNA-Seq, whole genome, whole exome, and targeted re-sequencing. * The analytical services and support platform aims to provide state-of-the-art assistance in the collection, management, analysis, and interpretation of large-scale data with a focus on data generated using ''''omic technologies. In addition, they offer software, services, and training designed to assist investigators in advancing their research. * The CCCB research program is focused on development of new methods for improving analysis and interpretation of genomic data through integration of diverse data types with the goal of creating open-source software tools to be made freely-available to the research community.

Proper citation: DFCI Center for Cancer Computational Biology (RRID:SCR_012688) Copy   

•   Source: SciCrunch Registry
  

http://www.ucl.ac.uk/biobank/

Two University College London (UCL) biobanks, one based at the Royal Free Hospital (RFH) Campus and the other based at Bloomsbury supporting Pathology and the Cancer Institute, will act as physical repositories for collections of biological samples and data from patients consented at UCLH, Partners Hospitals and external sources. This will incorporate collections of existing stored samples and new collections. UCL-RFH BioBank, the physical repository at the Royal Free, presents a unique opportunity to advance medical research through making access to research tissue easier, faster and much more efficient. The BioBank is both a physical repository, with capacity for up to 1 million cryogenically stored samples and a virtual repository for all tissue, cell, plasma, serum, DNA and RNA samples stored throughout UCLP. In particular, samples considered "relevant material", such as tissues and cells, that are licensed by the Human Tissue Authority, can be stored long term. Existing holdings of tissues and cells where appropriate can be transferred to the Physical BioBank at the Royal Free. UCL - Royal Free BioBank provides a flexible approach to banking, allowing the Depositor to pick and choose services that are tailored to fit their requirements. Collaborations arising from publicizing of the existence of the holdings are entirely at the discretion of the depositor, as the facility ensures that access to the deposits remains at the decision of the Depositor/User. UCL Biobank for studying Health and Disease (based at Pathology-Rockefeller building and the UCL-Cancer Institute will support projects principally involved in the study of human disease. The aim is to support primarily, research in the Pathology Department, UCLH and the UCL-Cancer Institute but it will also support other UCLH partners. The biobank will store normal and pathological specimens, surplus to diagnostic requirements, from relevant tissues and bodily fluids. Stored tissues will include; snap-frozen or cryopreserved tissue, formalin-fixed tissue, paraffin-embedded tissues, and slides prepared for histological examination. Tissues will include resection specimens obtained surgically or by needle core biopsy. Bodily fluids will include; whole blood, serum, plasma, urine, cerebrospinal fluid, milk, saliva and buccal smears and cytological specimens such as sputum and cervical smears. Fine needle aspirates obtained from tissues and bodily cavities (e.g. pleura and peritoneum) will also be collected. Where appropriate the biobank will also store separated cells, protein, DNA and RNA isolated from collected tissues and bodily fluids described above. Some of the tissue and aspirated samples will be stored in the diagnostic archive.

Proper citation: UCL Biobank (RRID:SCR_000517) Copy   

•   Source: SciCrunch Registry
  

http://cancergenome.nih.gov/

Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).

Proper citation: The Cancer Genome Atlas (RRID:SCR_003193) Copy   

•   Source: SciCrunch Registry