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http://www.qcmg.org/bioinformatics/tiki-index.php

A single nucleotide variant caller optimised for identifying somatic variants in low cellularity cancer samples.

Proper citation: qSNP (RRID:SCR_005105) Copy   

•   Source: SciCrunch Registry

http://statgenpro.psychiatry.hku.hk/limx/kggseq/

A biological Knowledge-based mining platform for Genomic and Genetic studies using Sequence data. The software platform, constituted of bioinformatics and statistical genetics functions, makes use of valuable biologic resources and knowledge for sequencing-based genetic mapping of variants / genes responsible for human diseases / traits. It facilitates geneticists to fish for the genetic determinants of human diseases / traits in the big sea of DNA sequences. KGGSeq has paid attention to downstream analysis of genetic mapping. The framework was implemented to filter and prioritize genetic variants from whole exome sequencing data.

Proper citation: KGGSeq (RRID:SCR_005311) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.janssenrnd.com/

Pharmaceutical company with a mission to discover and develop innovative medicines that ease patients'' suffering, and solve the most important unmet medical needs of our time. As one of the Janssen Pharmaceutical Companies, their strategy is to identify the biggest unmet medical needs and match them with the best science, internal or external, to find solutions for patients worldwide. They leverage their world-class discovery and development expertise, and operational excellence, to bring innovative, effective treatments in five therapeutic areas: cardiovascular and metabolism, immunology, infectious diseases and vaccines, neuroscience, and oncology.

Proper citation: Janssen Research and Development (RRID:SCR_003904) Copy   

•   Source: SciCrunch Registry

http://www.althia.es/

A biomedical company in Spain focused on the development of new tools for diagnosis and personalized treatment of oncological diseases and precancerous. It has three areas of activity in permanent innovation: Assistance in Diagnosis / Prognosis in solid and hematological tumors, Translational Research covering the gap between basic and clinical research and advanced radiotherapy treatments based image-guided single dose (SD-IGRT). Their objectives are: * Provide an integrated tissue and tumor molecular phenotype analysis using the most advanced technologies in diagnosis, to guide the most appropriate treatment for each patient. * Discover and validate molecular patterns by molecular systems and platforms including pathology, to generate predictive algorithms through computational biology, evolution and determining the response of patients with a particular tumor profile. Althia has laboratories equipped with the most advanced equipment and technologies in Barcelona and Granada Genyo Center, with offices in Madrid.

Proper citation: Althia (RRID:SCR_003918) Copy   

•   Source: SciCrunch Registry

http://datacatalog.med.nyu.edu/

A searchable data catalog that facilitates researchers'' access to large datasets available either publicly or through institutional or individual licensing. Dataset records include information about the content of the dataset, how to access the dataset, and local experts within NYULMC and NYU to assist in the use of these datasets. The data catalog will expand to include internally generated datasets from NYULMC and NYU in the near future. Use the contact form if you are interested in submitting a dataset to the data catalog.

Proper citation: NYU Data Catalog (RRID:SCR_004012) Copy   

•   Source: SciCrunch Registry

http://www.epo-berlin.com/

A small and medium-sized enterprise (SME) that has expertise in preclinical pharmacology, pharmacokinetics, and toxicology for the characterization of novel anticancer therapeutics and predictive biomarkers like: cytostatics, biologicals (peptides, antibodies), (anti)-hormones, immunomodulators (cytokines), and gene therapeutics. EPO has modern laboratories licensed for animal experiments and gene technology (S2) and a broad panel of murine and human tumor models growing in immunocompetent (SPF-quality, syngeneic strains) or immunodeficient mice (nude, SCID, NOD/SCID). EPO has established imaging technologies to monitor in vivo tumor growth.

Proper citation: Experimental Pharmacology and Oncology Berlin-Buch (RRID:SCR_003954) Copy   

•   Source: SciCrunch Registry

http://www.alacris.de/

Commercial organization that uses next generation sequencing technologies coupled with computational modeling of tumor and somatic tissues in order to identify individualized therapies for cancer patients. The company also uses these technologies to help pharmaceutical partners stratify patients for their clinical trials. Alacris has an exclusive worldwide commercial license for the computational modeling of tumors and somatic tissues using proprietary computational systems modeling technologies ModCell developed at the Max Planck Institute for Molecular Genetics (MPI-MG) in Berlin coupled with next generation sequencing and genotyping technology developed at Harvard Medical School in Boston. The company also is building up the first next generation sequencing center in Europe for clinical operations.

Proper citation: Alacris Theranostics (RRID:SCR_003953) 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   

•   Source: SciCrunch Registry

https://hoosiercancer.org/

An independent nonprofit cancer research organization that provides full-service clinical trial management and support, from conception and study design through project completion and publication. Established to explore and develop leading edge cancer treatments across the United States and internationally, their clinical trials, developed in collaboration with academic and community oncologists, are conducted within a member network of more than 130 clinical research sites. Their vision and mission is to form unparalleled relationships between academic, community, pharmaceutical, and biotech partners with the goal of advancing cancer research, education, and patient advocacy. There are no costs to become a member.

Proper citation: Hoosier Cancer Research Network (RRID:SCR_004026) Copy   

•   Source: SciCrunch Registry

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

http://www.ncibi.org/

The Center develops conceptual models, computational infrastructure, an integrated knowledge repository, and query and analysis tools that enable scientists to effectively access and integrate the wealth of biological data. The National Center for Integrative Biomedical Informatics (NCIBI) was founded in October 2005 and is one of seven National Centers for Biomedical Computing (NCBC) in the NIH Roadmap. NCIBI is based at the University of Michigan as a part of the Center for Computational Medicine and Biology (CCMB). NCIBI is composed of biomedical researchers, computational biologists, computer scientists, developers and human-computer interaction specialists organized into seven major core functions. They work in interdisciplinary teams to collectively develop tools that are not only computationally powerful but also biologically relevant and meaningful. The four initial Driving Biological Projects (prostate cancer progression, Type 1 and type 2 diabetes and bipolar disorder) provide the nucleation point from which tool development is informed, launched, and tested. In addition to testing tools for function, a separate team is dedicated to testing usability and user interaction that is a unique feature of this Center. Once tools are developed and validated the goal of the Center is to share and disseminate data and software throughout the research community both internally and externally. This is achieved through various mechanisms such as training videos, tutorials, and demonstrations and presentations at national and international scientific conferences. NCIBI is supported by NIH Grant # U54-DA021519.

Proper citation: National Center for Integrative Biomedical Informatics (RRID:SCR_001538) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/SamSPECTRAL.html

Software that identifies cell population in flow cytometry data. It demonstrates significant advantages in proper identification of populations with non-elliptical shapes, low density populations close to dense ones, minor subpopulations of a major population and rare populations. It samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting connected components estimate biological cell populations in the data sample.

Proper citation: SamSPECTRAL (RRID:SCR_001858) Copy   

•   Source: SciCrunch Registry

https://cansar.icr.ac.uk/

canSAR is an integrated database that brings together biological, chemical, pharmacological (and eventually clinical) data. Its goal is to integrate this data and make it accessible to cancer research scientists from multiple disciplines, in order to help with hypothesis generation in cancer research and support translational research. This cancer research and drug discovery resource was developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data.

Proper citation: canSAR (RRID:SCR_006794) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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

A coding system for reporting adverse events that occur in the course of cancer therapy. It was derived from the Common Toxicity Criteria (CTC) v2.0 and is maintained by the Cancer Therapy Evaluation Program (CTEP) at the National Cancer Institution (NCI).

Proper citation: Common Terminology Criteria for Adverse Events (RRID:SCR_010296) Copy   

•   Source: SciCrunch Registry

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

Upper-level ontology for cancer.

Proper citation: Upper-Level Cancer Ontology (RRID:SCR_010443) Copy   

•   Source: SciCrunch Registry

http://www.ariad.com/

Cambridge, Massachusetts-based biotechnology company focused on cancer. Focus areas are blood cancers and solid tumors. Compounds: ponatinib, AP26113, ridaforolimus and AP1903., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ARIAD (RRID:SCR_008559) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/drgap/

Designed to identify Driver Genes and Pathways in cancer genome sequencing studies.

Proper citation: DrGaP (RRID:SCR_008670) Copy   

•   Source: SciCrunch Registry

http://pub.ist.ac.at/ttp/

Software used to simulate tumor progression in various stages of growth in order to study the process' dynamics. The input can be fitness landscape, mutation rate, and cell division time. The output is growth dynamics and other relevant statistics, such as expected tumor detection time and expected appearance time of surviving mutants. The tool is implemented in Java and runs on all operating systems which run a Java Virtual Machine (JVM) of version 1.7 or above.

Proper citation: Tool for Tumor Progression (RRID:SCR_014700) Copy   

•   Source: SciCrunch Registry