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http://www.broadinstitute.org/science/programs/genome-biology/computational-rd/somaticcall-manual

Software program that finds single-base differences (substitutions) between sequence data from tumor and matched normal samples. It is designed to be highly stringent, so as to achieve a low false positive rate. It takes as input a BAM file for each sample, and produces as output a list of differences (somatic mutations). Note: This software package is no longer supported and information on this page is provided for archival purposes only.

Proper citation: SomaticCall (RRID:SCR_001196) Copy   

•   Source: SciCrunch Registry

http://www.mmnt.net/db/0/0/ftp-genome.wi.mit.edu/distribution/GISTIC2.0

Software to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, they improve the estimation of background rates for each category.

Proper citation: GISTIC (RRID:SCR_000151) Copy   

•   Source: SciCrunch Registry

http://www.oncotest.com/

A contract research organization (CRO) specializing in preclinical oncology services. As a pioneer in the field of patient derived tumor xenografts (PDX), they provide tailored solutions to the problems faced by preclinical oncology researchers. They assist with the identification of the best drug candidates and the validation of their targets and deliver in-depth bioinformatics analyses, laying the groundwork for the successful planning of clinical trials. Their diverse tumor model collection enables them to recommend the right assays and models to answer their customers' questions. Their AAALAC accredited facilities with IVC system, separate model development unit, large cage capacity of over 14,500 mice and proprietary electronic measurement system with an integrated database and by continuously maintaining important PDX models in mice, they are able to provide the highest standard of testing within a reasonable timeframe.

Proper citation: Oncotest (RRID:SCR_000489) Copy   

•   Source: SciCrunch Registry

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

Software for the reliable and accurate identification of somatic point mutations in next generation sequencing data of cancer genomes.

Proper citation: MuTect (RRID:SCR_000559) Copy   

•   Source: SciCrunch Registry

http://patchwork.r-forge.r-project.org/

Software tool for analyzing and visualizing allele-specific copy numbers and loss-of-heterozygosity in cancer genomes. The data input is in the format of whole-genome sequencing data which enables characterization of genomic alterations ranging in size from point mutations to entire chromosomes. High quality results are obtained even if samples have low coverage, ~4x, low tumor cell content or are aneuploid. Patchwork takes BAM files as input whereas PatchworkCG takes input from CompleteGenomics files. TAPS performs the same analysis as Patchwork but for microarray data.

Proper citation: Patchwork (RRID:SCR_000072) 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

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

https://wiki.nci.nih.gov/display/cageneindex/Cancer+Gene+Index+End+User+Documentation

THIS RESOURCE IS NO LONGER IN SERVICE, documented on November 17, 2016. A database of genes that have been experimentally associated with human cancer diseases and/or pharmacological compounds, the evidence of these associations, and relevant annotations on the data.

Proper citation: Cancer Gene Index (RRID:SCR_001117) Copy   

•   Source: SciCrunch Registry

https://seer.cancer.gov/statfacts/

Collection of statistical summaries for number of common cancer types. They were developed to provide quick overview of frequently requested cancer statistics. Available statistics may include incidence, mortality, survival, stage, prevalence, and lifetime risk. Links to additional resources from NCI including risk factors, treatment, and clinical trials are also provided. The statistics will be updated annually to coincide with the SEER data release.

Proper citation: National Cancer Institute Cancer Statistics Cancer Stat Facts (RRID:SCR_024437) Copy   

•   Source: SciCrunch Registry

http://cahub.cancer.gov/about/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented July 5, 2018. A national center for biospecimen science and standards to advance cancer research and treatment. It was created in response to the critical and growing need for high-quality, well-documented biospecimens for cancer research. The initiative builds on resources already developed by the NCI, including the Biospecimen Research Network and the NCI Best Practices for Biospecimen Resources, both of which were developed to address challenges around standardization of the collection and dissemination of quality biospecimens. caHUB will develop the infrastructure for collaborative biospecimen research and the production of evidence-based biospecimen standard operating procedures.

Proper citation: caHUB (RRID:SCR_009657) 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

https://med.stanford.edu/lucasmri.html

Biomedical technology research center that develops innovative technologies in five core research areas of magnetic resonance imaging and spectroscopy (MRI/MRS): # image reconstruction, fast imaging and radiofrequency (RF) pulse design methods, # R hardware development, # body imaging methods, # neuroimaging methods. # MR spectroscopy methods. In each of these areas, they capitalize on the long-standing, successful partnership and extensive experience in Stanford's Radiology and Electrical Engineering departments to improve and expand imaging technology for use in basic research and clinical care, and to provide cutting edge opportunities to the extramural community for biomedical research with MRI. Over its more than 18 years of existence, CAMRT has been motivated by and has served a wide base of extramurally sponsored collaborators and service users from leading medical and research institutions. Examples of collaborative projects are the development of real-time functional MRI biofeedback methods for neuroscience and clinical applications such as pain remediation, development of methods to mitigate metal artifacts in musculoskeletal imaging, development of novel RF pulses for many applications, and studies of breast cancer with efficient MRS methods.

Proper citation: Richard M. Lucas Center for Imaging (RRID:SCR_001406) Copy   

•   Source: SciCrunch Registry

http://www.utsouthwestern.edu/education/medical-school/departments/airc/southwestern-nmr-center/index.html

Biomedical technology research center that develops and applies new methods for analysis of metabolic networks in intact tissues, animals and human patients. The importance of understanding abnormal metabolism in common diseases such as cancer, diabetes and heart disease has long been appreciated. Because of constraints in technology, however, much of this research has been conducted in isolated systems where clinical relevance may be uncertain. Progress in magnetic resonance technology provides a foundation for major advances towards new ways of imaging metabolism in patients. These new techniques offer the advantage of imaging biochemical pathways without radiation. The focus of this Resource is to bring these technologies to a level where clinical research is feasible through the development of new MR contrast agents, NMR spectroscopy at high fields, and imaging of hyperpolarized 13C.

Proper citation: Southwestern NMR Center for In Vivo Metabolism (RRID:SCR_001429) Copy   

•   Source: SciCrunch Registry

http://ranchobiosciences.com/gse27831/

Curated data set from gene expression profiles of 29 unique samples from uveal melanoma patients that were measured on Affymetrix microarray. In addition, expression of syntenin-1 was measured by RT-PCR and this data is also available in the study.

Proper citation: GSE27831 (RRID:SCR_003646) Copy   

•   Source: SciCrunch Registry

http://ranchobiosciences.com/gse20194/

Curated data set of gene expression data from 230 stage I-III breast cancers that were generated from fine needle aspiration specimens of newly diagnosed breast cancers before any therapy. The biopsy specimens were collected sequentially during a prospective pharmacogenomic marker discovery study between 2000 and 2008. These specimens represent 70-90% pure neoplastic cells with minimal stromal contamination. In the study, patients received 6 months of preoperative (neoadjuvant) chemotherapy including paclitaxel, 5-fluorouracil, cyclophosphamide and doxorubicin followed by surgical resection of the cancer.

Proper citation: GSE20194 (RRID:SCR_003645) Copy   

•   Source: SciCrunch Registry

http://ranchobiosciences.com/gse4698/

Curated data set where gene expression profiling was performed on 60 prospectively collected samples of children with first relapse of acute lymphoblastic leukemia enrolled on the relapse trial ALL-REZ BFM 2002 of the Berlin-Frankfurt-Muenster study group.

Proper citation: GSE4698 (RRID:SCR_003644) Copy   

•   Source: SciCrunch Registry

http://ranchobiosciences.com/gse4271/

Curated data set from a study that investigated 77 primary high-grade astrocytomas and 23 matched recurrences so that changes in gene expression related to both survival and disease progression can be identified. Samples in the study include WHO grade III and IV astrocytomas with a wide range of survival times.

Proper citation: GSE4271 (RRID:SCR_003643) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/~rnusse/pathways/targets.html

A list of target genes of Wnt/beta-catenin signaling. Suggestions for additions are welcome. Direct targets are defined as those with Tcf binding sites and demonstrating that these sites are important.

Proper citation: Target genes of Wnt/beta-catenin signaling (RRID:SCR_007022) Copy   

•   Source: SciCrunch Registry