Tools   Select Another Resource Report Type

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.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.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.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

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

https://www.bannerhealth.com/research/locations/sun-health-institute/programs/body-donation

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. An autopsy-based, research-devoted brain bank, biobank and biospecimen bank that derives its human donors from the Arizona Study of Aging and Neurodegenerative Disease (AZSAND), a longitudinal clinicopathological study of the health and diseases of elderly volunteers living in Maricopa county and metropolitan Phoenix, Arizona. Their function is studied during life and their organs and tissue after death. To date, they have concentrated their studies on Alzheimer's disease, Parkinson's disease, heart disease and cancer. They share the banked tissue, biomaterials and biospecimens with qualified researchers worldwide. Registrants with suitable scientific credentials will be allowed access to a database of available tissue linked to relevant clinical information, and will allow tissue requests to be initiated., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Brain and Body Donation Program (RRID:SCR_004822) Copy   

•   Source: SciCrunch Registry

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

A software tool for identifying structural variation in tumour genomes from whole genome illumina sequencing.

Proper citation: deStruct (RRID:SCR_004747) Copy   

•   Source: SciCrunch Registry

https://www.saintluc.be/en/node/2561

An essential reference center in Europe and a leader in French-speaking Belgium that treats all types of adult and childhood cancer. They fight against cancer while giving patients comprehensive and humane care. Their quest for excellence is in three main academic fields: clinical care, research and teaching.

Proper citation: Cliniques Universitaires Saint-Luc Cancer Centre (RRID:SCR_004922) Copy   

•   Source: SciCrunch Registry

http://glioblastoma.alleninstitute.org/

Platform for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels that includes two interactive databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities: * The open public image database, here, providing in situ hybridization data mapping gene expression across the anatomic structures inherent in glioblastoma, as well as associated histological data suitable for neuropathological examination * A companion database (Ivy GAP Clinical and Genomic Database) offering detailed clinical, genomic, and expression array data sets that are designed to elucidate the pathways involved in glioblastoma development and progression. This database requires registration for access. The hope is that researchers all over the world will mine these data and identify trends, correlations, and interesting leads for further studies with significant translational and clinical outcomes. The Ivy Glioblastoma Atlas Project is a collaborative partnership between the Ben and Catherine Ivy Foundation, the Allen Institute for Brain Science and the Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment.

Proper citation: Ivy Glioblastoma Atlas Project (RRID:SCR_005044) Copy   

•   Source: SciCrunch Registry

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

Software to estimate purity / ploidy, and from that compute absolute copy-number and mutation multiplicities. When DNA is extracted from an admixed population of cancer and normal cells, the information on absolute copy number per cancer cell is lost in the mixing. The purpose of ABSOLUTE is to re-extract these data from the mixed DNA population. This process begins by generation of segmented copy number data, which is input to the ABSOLUTE algorithm together with pre-computed models of recurrent cancer karyotypes and, optionally, allelic fraction values for somatic point mutations. The output of ABSOLUTE then provides re-extracted information on the absolute cellular copy number of local DNA segments and, for point mutations, the number of mutated alleles.

Proper citation: ABSOLUTE (RRID:SCR_005198) Copy   

•   Source: SciCrunch Registry

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

https://htrn.osu.edu/Pages/Default.aspx

Collect, bank, and distribute human tissue and fluid specimens by uniting tissue-based research resources within the OSU Department of Pathology and promoting collaborative research within the OSU Medical Center and related national human research projects. The HTRN is comprised of the Pathology Core Facility (PCF), Tissue Archive Service (TAS), Tissue Procurement Service (TPS), AIDS and Cancer Specimen Resource (ACSR), the Cancer and Leukemia Group B Pathology Coordinating Office (CALGB - PCO), and an Adenoma Polyp Tissue Bank (APTB).

Proper citation: Human Tissue Resource Network (RRID:SCR_005348) 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

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