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http://www.ibridgenetwork.org/wustl/splinter

Software that detects and quantifies short IN/DELs as well as single nucleotide substitutions in pooled-DNA samples.

Proper citation: SPLINTER (RRID:SCR_005826) Copy   

•   Source: SciCrunch Registry
  

http://www.cancer.org/

The American Cancer Society is the nationwide, community-based, voluntary health organization dedicated to eliminating cancer as a major health problem by preventing cancer, saving lives, and diminishing suffering from cancer, through research, education, advocacy, and service. Together with our millions of supporters, the American Cancer Society (ACS) saves lives and creates a world with less cancer and more birthdays by helping people stay well, helping people get well, by finding cures, and by fighting back. Headquartered in Atlanta, Georgia, the ACS has 12 chartered Divisions, more than 900 local offices nationwide, and a presence in more than 5,100 communities.

Proper citation: American Cancer Society (RRID:SCR_005756) Copy   

•   Source: SciCrunch Registry
  

https://array.nci.nih.gov/caarray/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on Sep 18, 2018. Open-source, web and programmatically accessible microarray data management system. caArray guides the annotation and exchange of array data using a federated model of local installations whose results are shareable across the cancer Biomedical Informatics Grid (caBIG). caArray furthers translational cancer research through acquisition, dissemination and aggregation of semantically interoperable array data to support subsequent analysis by tools and services on and off the Grid. As array technology advances and matures, caArray will extend its logical library of assay management.

Proper citation: caArray (RRID:SCR_006053) 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://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.ivyfoundation.org/

Funds patient-focused research on gliomas to develop better diagnostics and treatments that lead to long-term survival and a high quality of life for patients with brain tumors. The goal is to decrease the suffering of patients with brain tumors. With an ultimate goal to cure brain cancer, their immediate goal is to improve diagnostics and treatment. They are dedicated to improving the lives of all patients with brain cancer by funding research that they hope will lead to the doubling of life expectancy of patients with brain cancer. Their goal is to do this within the next seven years. Since 2005 they''ve committed more than $50 million to research into brain tumors, with the expectation that this will lead to better diagnostics and therapies. They are dedicated to this search because funding leads to answers, and answers lead to hope.

Proper citation: Ben and Catherine Ivy Foundation (RRID:SCR_006333) Copy   

•   Source: SciCrunch Registry
  

http://www.biogrid.org.au

A federated data sharing platform and infrastructure that provides access to real-time clinical, imaging and biospecimen data across jurisdictions, institutions and diseases. The web-based platform provides a secure infrastructure that advances health research by linking privacy-protected and ethically approved data among a wide network of health collaborators. Access to de-identified health records data is granted to authorized researchers after an application process so patient privacy and intellectual property are protected. BioGrid Australia''s approved researchers are provided access to multiple institutional databases, via the BioGrid interface, preventing gaps in patient records and research analysis. This legal and ethical arrangement with participating collaborators allows BioGrid to connect data through a common platform where data governance and access is managed by a highly skilled team. Data governance, security and ethics are at the core of BioGrid''s federated data sharing platform that securely links patient level clinical, biospecimen, genetic and imaging data sets across multiple sites and diseases for the purpose of medical research. BioGrid''s infrastructure and data management strategies address the increasing need by authorized researchers to dynamically extract and analyze data from multiple sources whilst protecting patient privacy. BioGrid has the capability to link data with other datasets, produce tailored reports for auditing and reporting and provide statistical analysis tools to conduct more advanced research analysis. In the health sector, BioGrid is a trusted independent virtual real-time data repository. Government investment in BioGrid has facilitated a combination of technology, collaboration and ethics approval processes for data sharing that exist nowhere else in the world.

Proper citation: BioGrid Australia (RRID:SCR_006334) Copy   

•   Source: SciCrunch Registry
  

http://bccancerfoundation.com/

The fundraising partner of the BC Cancer Agency and the largest charitable funder of cancer research in this province. We enable donors to make contributions to leading-edge research that has a direct impact on improvements to cancer care for patients in British Columbia. By connecting philanthropy and research, we support the revolutionary advances that will help us achieve our vision of a world free from cancer. Our Cause: Supporting world-renowned cancer research in BC One in three British Columbians will be diagnosed with cancer in their lifetime. Because the BC Cancer Agency integrates research and treatment, the process from discovery to treatment is shorter, and researchers are asking questions focused on the needs of patients in BC. The BC Cancer Agency excels at translating exciting discoveries from the researcher''''s bench to the patient''''s bedside. This is the quickest and most effective way to impact cancer care, and it makes the Agency an international leader in cancer research. Our Donors: Leading the way in helping us advance our cause Our unique relationship with the BC Cancer Agency allows our donors to be part of research discoveries made right here in BC. We invite donors to become more connected to the BC Cancer Agency''''s world-leading research that is shaping the future of patient care. At the BC Cancer Foundation we are funding the areas of greatest priority and promise. We know, because we ask the scientists and clinicians at the BC Cancer Agency to identify the priorities and needs that will have the most significant and timely impact on cancer care and treatment. We then partner with our donors to raise the necessary funds in support of this life-saving work. Together we are funding and finding solutions that are having a direct impact on improving care for cancer patients in British Columbia.

Proper citation: BC Cancer Foundation (RRID:SCR_006428) 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
  

https://sites.google.com/site/oncosnp/

An analytical software tool for characterizing copy number alterations and loss-of-heterozygosity (LOH) events in cancer samples from SNP genotyping data.

Proper citation: OncoSNP (RRID:SCR_012985) Copy   

•   Source: SciCrunch Registry
  

http://www.genesigdb.org

Database of traceable, standardized, annotated gene signatures which have been manually curated from publications that are indexed in PubMed. The Advanced Gene Search will perform a One-tailed Fisher Exact Test (which is equivalent to Hypergeometric Distribution) to test if your gene list is over-represented in any gene signature in GeneSigDB. Gene expression studies typically result in a list of genes (gene signature) which reflect the many biological pathways that are concurrently active. We have created a Gene Signature Data Base (GeneSigDB) of published gene expression signatures or gene sets which we have manually extracted from published literature. GeneSigDB was creating following a thorough search of PubMed using defined set of cancer gene signature search terms. We would be delighted to accept or update your gene signature. Please fill out the form as best you can. We will contact you when we get it and will be happy to work with you to ensure we accurately report your signature. GeneSigDB is capable of providing its functionality through a Java RESTful web service.

Proper citation: GeneSigDB (RRID:SCR_013275) Copy   

•   Source: SciCrunch Registry
  

http://www.bioconductor.org/packages//2.10/bioc/html/CancerMutationAnalysis.html

Software package that implements gene and gene-set level analysis methods for somatic mutation studies of cancer.

Proper citation: CancerMutationAnalysis (RRID:SCR_013181) Copy   

•   Source: SciCrunch Registry
  

https://omictools.com/l2l-tool

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on August 26, 2019.

Database of published microarray gene expression data, and a software tool for comparing that published data to a user''''s own microarray results. It is very simple to use - all you need is a web browser and a list of the probes that went up or down in your experiment. If you find L2L useful please consider contributing your published data to the L2L Microarray Database in the form of list files. L2L finds true biological patterns in gene expression data by systematically comparing your own list of genes to lists of genes that have been experimentally determined to be co-expressed in response to a particular stimulus - in other words, published lists of microarray results. The patterns it finds can point to the underlying disease process or affected molecular function that actually generated the observed changed in gene expression. Its insights are far more systematic than critical gene analyses, and more biologically relevant than pure Gene Ontology-based analyses. The publications included in the L2L MDB initially reflected topics thought to be related to Cockayne syndrome: aging, cancer, and DNA damage. Since then, the scope of the publications included has expanded considerably, to include chromatin structure, immune and inflammatory mediators, the hypoxic response, adipogenesis, growth factors, hormones, cell cycle regulators, and others. Despite the parochial origins of the database, the wide range of topics covered will make L2L of general interest to any investigator using microarrays to study human biology. In addition to the L2L Microarray Database, L2L contains three sets of lists derived from Gene Ontology categories: Biological Process, Cellular Component, and Molecular Function. As with the L2L MDB, each GO sub-category is represented by a text file that contains annotation information and a list of the HUGO symbols of the genes assigned to that sub-category or any of its descendants. You don''''t need to download L2L to use it to analyze your microarray data. There is an easy-to-use web-based analysis tool, and you have the option of downloading your results so you can view them at any time on your own computer, using any web browser. However, if you prefer, the entire L2L project, and all of its components, can be downloaded from the download page. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: L2L Microarray Analysis Tool (RRID:SCR_013440) 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
  

http://sharedresources.fredhutch.org/core-facilities/bioinformatics

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on July 27,2022. Core provides bioinformatics specialists available to assist researchers with processing, exploring, and understanding genomics data.

Proper citation: Fred Hutchinson Cancer Research Center Co-operative Center for Excellence in Hematology Bioinformatics Resource (RRID:SCR_015324) Copy   

•   Source: SciCrunch Registry
  

http://gepia.cancer-pku.cn

Web server for cancer and normal gene expression profiling and interactive analyses. Interactive web server for analyzing RNA sequencing expression data of tumors and normal samples from TCGA and GTEx projects, using standard processing pipeline. Provides customizable functions such as tumor or normal differential expression analysis, profiling according to cancer types or pathological stages, patient survival analysis, similar gene detection, correlation analysis and dimensionality reduction analysis.

Proper citation: Gene Expression Profiling Interactive Analysis (RRID:SCR_018294) Copy   

•   Source: SciCrunch Registry
  

https://www.tissue-atlas.org/

Gathers together imaging and omic datasets into molecular maps of normal and diseased tissue from human and animal models, with emphasis on cancer. Used to access datasets, educational curriculum and talks, and recommended methods and software.

Proper citation: Harvard Tissue Atlas (RRID:SCR_022829) Copy   

•   Source: SciCrunch Registry
  

https://tabula-sapiens-portal.ds.czbiohub.org/

Single cell transcriptomic atlas of multiple organs from individual human donors. Multiple organ, single cell transcriptomic atlas of humans. Molecular reference atlas for cell types of human body. Provides molecular definition of these cell types and reveals many other aspects of human biology, including how same gene can be spliced differently in different cell types, how shared cell types in different tissues can have subtle differences in their identities, and how clones of immune system can be shared across tissues.

Proper citation: Tabula Sapiens (RRID:SCR_022314) Copy   

•   Source: SciCrunch Registry