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https://code.google.com/p/nfuse/

Software that predicts fusion transcripts and associated CGRs from matched RNA-seq and Whole Genome Shotgun Sequencing (WGSS).

Proper citation: nFuse (RRID:SCR_000066) Copy   

•   Source: SciCrunch Registry

http://bsec.ornl.gov/AdaptiveCrawler.shtml

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 9,2022. A web crawler that can intelligently acquire social media content on the Internet to meet the specific online data source acquisition needs of cancer researchers.

Proper citation: AdaptiveCrawler (RRID:SCR_000573) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/group/nusselab/cgi-bin/wnt/

A resource for members of the Wnt community, providing information on progress in the field, maps on signaling pathways, and methods. The page on reagents lists many resources generously made available to and by the Wnt community. Wnt signaling is discussed in many reviews and in a recent book. There are usually several Wnt meetings per year.

Proper citation: Wnt homepage (RRID:SCR_000662) Copy   

•   Source: SciCrunch Registry

https://www.athensresearch.com/

Commercial supplier of bioproducts for studies of inflammation, autoimmune disease, cancer, coronary disease, Alzheimer's Disease and more. These include antibodies, enzymes, coagulation factors, and assay kits.

Proper citation: Athens Research and Technology (RRID:SCR_001079) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/devel/bioc/html/VegaMC.html

Software package that enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. It performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, it produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported.

Proper citation: VegaMC (RRID:SCR_001267) Copy   

•   Source: SciCrunch Registry

https://cismm.web.unc.edu/

Biomedical technology research center that develops force technologies applicable over a wide range of biological settings, from the single molecule to the tissue, with integrated systems that orchestrate facile instrument control, multimodal imaging, and analysis through visualization and modeling. The Force Microscope Technologies Core designs instruments in an area of science where there are unusual opportunities: the measurement of forces and the integration with optical microscopy. Force technologies play the obvious role of both measuring events in the sample and modifying the sample during the experiment. It is through the microscope that the force data is correlated with simultaneous 3D optical images. The force technology development includes the magnetic bead technology in the 3D Force Microscope project, Atomic Force Microscopy in the nanoManipulator project, and Control Software to drive the instrumentation. This core is focused on providing the physical capability to perform the experiments and probe structure/property correlations. The Ideal User Interfaces core makes the connection between the user and the instrument, the model building, and the data. This includes control systems that allow the user to move the bead inside the cell culture with a handheld pen and the visualization techniques to view the optical microscope data as a rendered 3D image collocated with the force data. Using data to create, change, and understand a model is the focus of the Advanced Model Fitting and Analysis core. The quantitative reduction of images to structural, shape, and velocity parameters is the goal of Image Analysis. The immediate understanding of correlations across image fields and between data sets in the challenge of Visualization. The power of combining the strength of a computer science graphics group with a microscopy technology group is most evident in the Graphics Hardware Acceleration project, which seeks to harness the speed of graphics processors for microscope data analysis and simulation. The Advanced Technology core pushes the boundaries of the Human Computer Interface through the investigation of improved techniques for the interaction of users with virtual environments, the real time lighting of virtual settings, and the enabling of multi-person collaboration. These techniques are validated and evaluated through physiological measures in virtual environments effectiveness evaluation studies.

Proper citation: Computer Integrated Systems for Microscopy and Manipulation (RRID:SCR_001413) Copy   

•   Source: SciCrunch Registry

http://www.cancergenomics.org/

Consortium promoting communication and collaboration among cancer cytogenomics laboratories, who are interested in applying microarray technologies to cancer diagnosis and cancer research. Their oals are to (1) establish platform-neutral and cancer specific microarray designs for diagnostic purposes, (2) share cancer microarray data between participating institutions for education purposes, (3) create a public cancer array database, and (4) carry out multicenter cancer genome translational research. Collaboration amongst the different laboratories and researchers will not only provide validation for the microarray design(s) but ultimately provide more comprehensive molecular information and more accurate interpretation to better serve cancer patients and further cancer research. The CGC was officially incorporated in June 2010 as a not-for-profit organization.

Proper citation: Cancer Genomics Consortium (RRID:SCR_002384) Copy   

•   Source: SciCrunch Registry

http://hardinmd.lib.uiowa.edu/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2, 2025. A medical database with lists, or directories, of information in health and medicine and images of medical conditions. Users may search Hardin MD, browse through the Medical picture gallery, and sort search results by disease or alphabetical letter.

Proper citation: Hardin MD (RRID:SCR_002364) 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

http://www.iro.umontreal.ca/~csuros/quadgt/

Software package for calling single-nucleotide variants in four sequenced genomes comprising a normal-tumor pair and the two parents. Genotypes are inferred using a joint model of parental variant frequencies, de novo germline mutations, and somatic mutations. The model quantifies the descent-by-modification relationships between the unknown genotypes by using a set of parameters in a Bayesian inference setting. Note that you can use it on any subset of the four related genomes, including parent-offspring trios, and normal-tumor pairs without parental samples.

Proper citation: QuadGT (RRID:SCR_000073) 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://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://pstiing.icr.ac.uk/

A publicly accessible knowledgebase about protein-protein, protein-lipid, protein-small molecules, ligand-receptor interactions, receptor-cell type information, transcriptional regulatory and signal transduction modules relevant to inflammation, cell migration and tumourigenesis. It integrates in-house curated information from the literature, biochemical experiments, functional assays and in vivo studies, with publicly available information from multiple and diverse sources across human, rat, mouse, fly, worm and yeast. The knowledgebase allowing users to search and to dynamically generate visual representations of protein-protein interactions and transcriptional regulatory networks. Signalling and transcriptional modules can also be displayed singly or in combination. This allow users to identify important "cross-talks" between signalling modules via connections with key components or "hubs". The knowledgebase will facilitate a "systems-wide" understanding across many protein, signalling and transcriptional regulatory networks triggered by multiple environmental cues, and also serve as a platform for future efforts to computationally and mathematically model the system behavior of inflammatory processes and tumourigenesis.

Proper citation: pSTIING (RRID:SCR_002045) Copy   

•   Source: SciCrunch Registry

http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.

Proper citation: COSMIC - Catalogue Of Somatic Mutations In Cancer (RRID:SCR_002260) Copy   

•   Source: SciCrunch Registry