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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://rulai.cshl.edu/cgi-bin/tools/ESE3/esefinder.cgi?process=home

A web-based resource that facilitates rapid analysis of exon sequences to identify putative exonic splicing enhancers (ESEs) responsive to the human SR proteins SF2/ASF, SC35, SRp40 and SRp55, and to predict whether exonic mutations disrupt such elements.

Proper citation: ESEfinder 3.0 (RRID:SCR_007088) Copy   

•   Source: SciCrunch Registry

http://www.cpc.unc.edu/projects/addhealth

Longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. Public data on about 21,000 people first surveyed in 1994 are available on the first phases of the study, as well as study design specifications. It also includes some parent and biomarker data. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The restricted-use contract includes four hours of free consultation with appropriate staff; after that, there''s a fee for help. Researchers can also share information through a listserv devoted to the database.

Proper citation: Add Health (National Longitudinal Study of Adolescent Health) (RRID:SCR_007434) Copy   

•   Source: SciCrunch Registry

http://www.oreganno.org/oregano/

Open source, open access database and literature curation system for community based annotation of experimentally identified DNA regulatory regions, transcription factor binding sites and regulatory variants. Automatically cross referenced against PubMED, Entrez Gene, EnsEMBL, dbSNP, eVOC: Cell type ontology, and Taxonomy database. Community driven resource for curated regulatory annotation.

Proper citation: Open Regulatory Annotation Database (RRID:SCR_007835) Copy   

•   Source: SciCrunch Registry

http://chtn.nci.nih.gov

The Cancer Diagnosis Program of the National Cancer Institute (NCI) initiated the Cooperative Human Tissue Network (CHTN) in 1987 to provide increased access to human tissue for basic and applied scientists from academia and industry to accelerate the advancement of discoveries in cancer diagnosis and treatment. This unique resource provides remnant human tissues and fluids from routine procedures to investigators who utilize human biospecimens in their research. Unlike tissue banks, the CHTN works prospectively with each investigator to tailor specimen acquisition and processing to meet their specific project requirements. Because the CHTN is funded by the NCI, the CHTN is able to maintain nominal processing fees for its services. The CHTN is comprised of five adult divisions and one pediatric division. Each of the adult divisions coordinates investigator applications/requests based upon the investigator's geographic location within North America. The Pediatric Division manages all investigators who request pediatric specimens only. The CHTN divisions share coordination for requests from outside North America. The CHTN divisions work both independently with individual investigators and together as a seamless unit to fulfill requests that are difficult to serve by any single division. The CHTN's unique informatics system allows each division to effectively communicate and network the needs of its investigators to all CHTN divisions. The Network as a whole can then help fulfill an investigator's request. Biospecimens from surgeries, autopsies and other routine procedures: Malignant, Benign, Diseased, Normal, Biofluids (urine, serum, plasma, buffy coat) High quality specimens at LOW processing fees: Fresh, Frozen, Floating in fixative, RNAlater, Paraffin embedded or and/or unstained slides, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Cooperative Human Tissue Network (RRID:SCR_004446) Copy   

•   Source: SciCrunch Registry

https://htrn.osu.edu/Services/Biorepository/Pages/default.aspx

The HTRN biospecimen bank is comprised of samples for the Ohio State University Cancer and Leukemia Group B Pathology Coordinating Office (CALGB-PCO) and the Ohio State University Midwestern Division of the Cooperative Human Tissue Network (CHTN). The CALGB-PCO banks biospecimens donated by patients enrolled in clinical trials. Samples can include tumor and normal tissue, plasma, serum, whole blood and white blood cells and urine. All of these samples are used later in correlative studies. The Midwestern Division of the CHTN stores a temporary biospecimen bank of tumor and normal tissue, tissue slides and paraffin embedded tissue blocks for research investigators throughout the country and Canada who are trying to find a cure for cancer. As part of the HTRN biospecimen bank, a Rees Scientific equipment monitoring system helps to secure the integrity and quality of samples stored in the biorepository. Scientific research within the HTRN is currently underway to determine the best methods in tissue storage for long term use. The NCI First-Generation Guidelines for NCI-Supported Biorepositories and the NCI Best Practices for Biospecimen Resources are continuously reviewed and adapted by the HTRN.

Proper citation: Ohio State Biorepository (RRID:SCR_004714) Copy   

•   Source: SciCrunch Registry

http://cancer.case.edu/sharedresources/tissue/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. The Case Comprehensive Cancer Center''s Biorepository and Tissue Processing Core Facility (BTPC) serves two primary functions: 1. To build an inventory of remnant human tissues, blood and other body fluids (collectively termed biospecimens) targeted towards cancer and other medical research, for later assignment to investigators; and 2. To provide long term, controlled storage of biospecimens for specific researchers. These samples are for research purposes only and may not be used for clinical diagnosis or implantation into humans. Clinical information relating to the samples and donors are collected and maintained in a secure database. Samples and data are de-identified or de-linked before release to the researcher unless he/she has specific IRB approval to gain access to this information. Remnant biospecimens are prospectively collected from surgical procedures, autopsies and clinical laboratories for the BTPC by the Human Tissue Procurement Facility (HTPF), which operates under UH-IRB Protocol 01-02-45. Blood and bone marrow specimens are collected for the BTPC by the Hematopoietic Stem Cell Core Facility (HSCC), which operates under UH-IRB Protocol 09-90-195. The Division of Surgical Pathology at University Hospitals Case Medical Center (UHCMC) has clinical archives of paraffin blocks that can be made available through the BTPC for retrospective research studies under the approval of the Vice Chair for Clinical Affairs at UHCMC. Surgical Pathologists associated with the BTPC are responsible for determining which blocks can be made available and how much material can be removed from the blocks. Types of Tissue Available * Malignant, benign, diseased, normal and normal human tissues * Normal adjacent tissues available paired with tumor specimens in many cases * Tissues are collected from over 50 anatomic sites * Frozen specimens, OCT-embedded and paraffin-embedded tissues * Large array of paraffin-embedded specimens from clinical archives of paraffin blocks and QC research blocks maintained by the HTPF * Peripheral blood and bone marrow samples from initial visits and follow-up procedures are processed to obtain serum and cell fractions for storage * No samples are collected from individuals with known infectious illnesses * Fetal biospecimens are not collected due to state and local statutes

Proper citation: Case Comprehensive Cancer Center Biorepository and Tissue Processing Core Facility (RRID:SCR_004382) Copy   

•   Source: SciCrunch Registry

http://www.uclaaidsinstitute.org/researchareas/clinical_malignancy.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 27, 2012. The National Cancer Institute established centers in the United States and its territories for the collection and distribution of tissues, blood and secretions from patients with clinically-characterized AIDS related malignancies in 1994. The AIDS Malignancy Bank makes these tissues available to qualified investigators in the United States for research on AIDS malignancies. It is hoped that by providing access to these high-quality specimens, research in AIDS-related malignancies will be encouraged and expanded. The AMB contains formalin-fixed paraffin-embedded tissues, fresh-frozen tissues, malignant-cell suspensions, fine-needle aspirates, and cell lines from AIDS-related malignancies. The bank also contains serum, plasma, urine, bone marrow, cervical secretions, anal swabs, saliva semen and multi-site autopsy tissues from patients with AIDS-related malignancies who have participated in clinical trials. The bank has an associated database that contains prognostic, staging, outcome and treatment data on patients from whom tissues were obtained. Researchers pay for preparation and shipping of specimens.

Proper citation: AIDS Malignancy Bank (RRID:SCR_004417) Copy   

•   Source: SciCrunch Registry

http://pilgrm.princeton.edu

PILGRM (the platform for interactive learning by genomics results mining) puts advanced supervised analysis techniques applied to enormous gene expression compendia into the hands of bench biologists. This flexible system empowers its users to answer diverse biological questions that are often outside of the scope of common databases in a data-driven manner. This capability allows domain experts to quickly and easily generate hypotheses about biological processes, tissues or diseases of interest. Specifically PILGRM helps biologists generate these hypotheses by analyzing the expression levels of known relevant genes in large compendia of microarray data. PILGRM is for the biologist with a set of proteins relevant to a disease, biological function or tissue of interest who wants to find additional players in that process. It uses a data driven method that provides added value for literature search results by mining compendia of publicly available gene expression datasets using lists of relevant and irrelevant genes (standards). PILGRM produces publication quality PDFs usable as supplementary material to describe the computational approach, standards and datasets. Each PILGRM analysis starts with an important biological question (e.g. What genes are relevant for breast cancer but not mammary tissue in general?). For PILGRM to discover relevant genes, it needs examples of both genes that you would (positive) and would not (negative) find interesting. Lists of these genes are what we call standards and in PILGRM you can build your own standards or you can use standards from common sources that we pre-load for your convenience. PILGRM lets you build your own literature-documented standards so that processes, disease, and tissues that are not well covered in databases of tissue expression, disease, or function can still be used for an analysis.

Proper citation: PILGRM (RRID:SCR_004749) Copy   

•   Source: SciCrunch Registry

http://www.scandb.org/newinterface/about.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. A large-scale database of genetics and genomics data associated to a web-interface and a set of methods and algorithms that can be used for mining the data in it. The database contains two categories of single nucleotide polymorphism (SNP) annotations: # Physical-based annotation where SNPs are categorized according to their position relative to genes (intronic, inter-genic, etc.) and according to linkage disequilibrium (LD) patterns (an inter-genic SNP can be annotated to a gene if it is in LD with variation in the gene). # Functional annotation where SNPs are classified according to their effects on expression levels, i.e. whether they are expression quantitative trait loci (eQTLs) for that gene. SCAN can be utilized in several ways including: (i) queries of the SNP and gene databases; (ii) analysis using the attached tools and algorithms; (iii) downloading files with SNP annotation for various GWA platforms. . eQTL files and reported GWAS from NHGRI may be downloaded., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SCAN (RRID:SCR_005185) Copy   

•   Source: SciCrunch Registry

http://rulai.cshl.edu/tred

Collects mammalian cis- and trans-regulatory elements together with experimental evidence. Regulatory elements were mapped on to assembled genomes. Resource for gene regulation and function studies. Users can retrieve primers, search TF target genes, retrieve TF motifs, search Gene Regulatory Networks and orthologs, and make use of sequence analysis tools. Uses databases such as Genbank, EPD and DBTSS, and employ promoter finding program FirstEF combined with mRNA/EST information and cross-species comparisons. Manually curated.

Proper citation: Transcriptional Regulatory Element Database (RRID:SCR_005661) 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.cbioportal.org/

A portal that provides visualization, analysis and download of large-scale cancer genomics data sets.

Proper citation: cBioPortal (RRID:SCR_014555) Copy   

•   Source: SciCrunch Registry

https://genome.ucsc.edu/cgi-bin/hgLiftOver

Web tool to convert genome coordinates and genome annotation files between assemblies. Used to translate genomic coordinates from one assembly version into another and retrieves putative orthologous regions in other species using UCSC chained and netted alignments.

Proper citation: liftOver (RRID:SCR_018160) Copy   

•   Source: SciCrunch Registry

https://github.com/brentp/mosdepth

Software command line tool for rapidly calculating genome wide sequencing coverage. Measures depth from BAM or CRAM files at either each nucleotide position in genome or for sets of genomic regions. Used for fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing quick coverage calculation for genomes and exomes.

Proper citation: mosdepth (RRID:SCR_018929) Copy   

•   Source: SciCrunch Registry

https://cran.rstudio.com/web/packages/accucor/index.html

Software as isotope natural abundance correction algorithm that is needed especially for high resolution mass spectrometers. Natural abundance correction of mass spectrometer data.

Proper citation: AccuCor (RRID:SCR_023046) Copy   

•   Source: SciCrunch Registry

http://www.nihpromis.org/

Repository of person centered measures that evaluates and monitors physical, mental, and social health in adults and children.

Proper citation: Patient-Reported Outcomes Measurement Information System (RRID:SCR_004718) Copy   

•   Source: SciCrunch Registry

http://ki.se/ki/jsp/polopoly.jsp?d=29332&a=23686&l=en

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. The original aim of this study was to increase our understanding of the etiology of malignant lymphomas, especially in view of the increasing trend in incidence. Malignant lymphoma (including non-Hodgkin lymphoma, NHL, Hodgkin lymphoma, HL, and chronic lymphocytic leukemia, CLL) constitute a heterogeneous group of malignancies with regard to histology, molecular characteristics and clinical course. Etiological factors may also vary by lymphoma subtype. The incidence of NHL, the most common lymphoma group, has increased dramatically during the past decades in Sweden and in many other Western countries. The reasons for this increase as well as for the majority of all new cases is not well understood. Well established risk factors for lymphoma overall include hereditary and acquired disorders of strong immune dysfunction such as HIV/AIDS and organ transplantation, but they explain few new cases in the population. Approach: Population-based case-control study in Sweden and Denmark. The study includes in total 3740 patients and 3187 controls in both countries recruited during the period October 1999 to October 2002. Through a rapid case ascertainment system, the cases were identified shortly after diagnosis. The controls were randomly selected from national population registers and frequency-matched to the expected number of cases by sex and age group. Both cases and controls were interviewed by telephone based on a standardized questionnaire to obtain detailed information on potential risk factors for lymphoma such as medical history including infectious diseases, drug use and blood transfusions, socio-economic factors and life-style. Blood samples were also collected and stored as serum, plasma, DNA and live lymphocytes. In addition, written questionnaires about dietary habits or work exposures were sent out in Sweden. Tumor material from the cases was re-examined and uniformly classified according to the REAL classification. Status The data collection ended in 2002 and data analysis has been ongoing since then. We have primarily analyzed a range of environmental factors in relation risk of malignant lymphoma subgroups including sun exposure, body mass index, family history of hematopoietic cancer, allergy, autoimmune disorders and mononucleosis. We have also assessed specific genetic determinants in a subgroups of patients with follicular lymphoma and controls. Study results have so far been presented in 14 publications in peer-reviewed journals. In addition to new analyses on other environmental factors, we now also work to understand genetic susceptibility and gene-environmental interaction and risk of lymphoma. Also, prognostic studies have been initiated in collaboration with other research groups with regard to in CLL, HL and T-cell lymphoma.

Proper citation: SCALE - Scandinavian lymphoma etiology (RRID:SCR_006041) Copy   

•   Source: SciCrunch Registry

https://maayanlab.cloud/chea3/

Web based transcription factor enrichment analysis. Web server ranks TFs associated with user-submitted gene sets. ChEA3 background database contains collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate composite rank that improves prediction of correct upstream TF compared to ranks produced by individual libraries.

Proper citation: ChIP-X Enrichment Analysis 3 (RRID:SCR_023159) Copy   

•   Source: SciCrunch Registry

https://kleintools.hms.harvard.edu/tools/spring.html

Interactive web tool to visualize single cell data using force directed graph layouts. Kinetic interface for visualizing high dimensional single cell expression data. Collection of pre-processing scripts and web browser based tool for visualizing and interacting with high dimensional data.

Proper citation: SPRING (RRID:SCR_023578) Copy   

•   Source: SciCrunch Registry