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https://software.broadinstitute.org/cancer/cga/polysolver

Software tool for HLA typing based on whole exome sequencing data and infers alleles for three major MHC class I genes. Enables accurate inference of germline alleles of class I HLA-A, B and C genes and subsequent detection of mutations in these genes using inferred alleles as reference.

Proper citation: Polysolver (RRID:SCR_022278) Copy   

•   Source: SciCrunch Registry

https://github.com/abyzovlab/CNVpytor

Software Python package and command line tool for CNV/CNA analysis from depth of coverage by mapped reads. Software tool for CNV/CNA detection and analysis from read depth and allele imbalance in whole genome sequencing.

Proper citation: CNVpytor (RRID:SCR_021627) Copy   

•   Source: SciCrunch Registry

https://cumulus.readthedocs.io/en/stable

Software tool as cloud based single cell genomics and spatial transcriptomics data analysis framework that is scalable to massive amounts of data and able to process variety of data types. Consists of cloud analysis workflow, Python analysis package and visualization application. Supports analysis of single-cell RNA-seq, CITE-seq, Perturb-seq, single-cell ATAC-seq, single-cell immune repertoire and spatial transcriptomics data.

Proper citation: Cumulus (RRID:SCR_021644) Copy   

•   Source: SciCrunch Registry

https://github.com/kukionfr/VAMPIRE_open

Software tool for analysis of cell and nuclear morphology from fluorescence or bright field images. Enables profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours. Robust method to quantify cell morphological heterogeneity.

Proper citation: VAMPIRE (RRID:SCR_021721) Copy   

•   Source: SciCrunch Registry

http://pinet-server.org

Web platform for downstream analysis and visualization of proteomics data. Server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. Primary input for server consists of set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values.

Proper citation: piNET (RRID:SCR_018693) Copy   

•   Source: SciCrunch Registry

https://cistrome.shinyapps.io/timer/

Web server for comprehensive analysis of tumor infiltrating immune cells. Web tool for systematical analysis of immune infiltrates across diverse cancer types. Allows users to input function specific parameters, with resulting figures dynamically displayed to access tumor immunological, clinical, and genomic features.

Proper citation: TIMER (RRID:SCR_018737) Copy   

•   Source: SciCrunch Registry

https://rosie.graylab.jhu.edu/docking2

Unified web framework for Rosetta applications. Web interface for selected Rosetta protocols. Web front end for Rosetta software suite. Provides common user interface for Rosetta protocols, stable application programming interface for developers to add additional protocols, flexible back-end to allow leveraging of computer cluster resources shared by Rosetta Commons member institutions, and centralized administration by Rosetta Commons to ensure continuous maintenance. Offers general and speedy paradigm for serverification of Rosetta applications. Lowers barriers to Rosetta use for broader biological community.

Proper citation: ROSIE (RRID:SCR_018764) Copy   

•   Source: SciCrunch Registry

https://delaney.shinyapps.io/CAIRN/

Web tool to graph all copy number alterations present in segment file. Custom data is permitted. Allows to display copy number alterations which overlap user specified region, to quantify number of amplified CNAs and deleted CNAs. Visualization tool to explore copy number alterations discovered in published cancer datasets. Intended to help oncology community observe of relative rates of amplification, deletion, and mutation of interesting genes and regions.

Proper citation: CAIRN (RRID:SCR_019101) Copy   

•   Source: SciCrunch Registry

https://www.robotreviewer.net/about

Open source web based system that uses machine learning and NLP to semi automate biomedical evidence synthesis, to aid practice of Evidence Based Medicine. Processes full text journal articles describing randomized controlled trials. Designed to automatically extract key data items from reports of clinical trials.

Proper citation: RobotReviewer (RRID:SCR_021064) Copy   

•   Source: SciCrunch Registry

https://www.synapse.org/#!Synapse:syn22345748/wiki/605339

Reference dataset of multiplexed immunofluorescence microscopy images collected at HMS Laboratory of Systems Pharmacology. Includes set of images of different types for development and benchmarking of computational methods for image processing. As of 4/2/2021, EMIT comprises tissue microarray containing cores from 34 cancer, non-neoplastic diseases, and normal tissue collected from clinical discards under IRB supervised protocol. TMA was imaged using cyclic immunofluorescence method. Additional extensions of EMIT are currently in the planning stages. Long term goal is to compose ImageNet like resource for highly multiplexed images of tissues and tumors by consolidating high quality curated datasets.

Proper citation: Exemplar Microscopy Images of Tissues (RRID:SCR_021052) Copy   

•   Source: SciCrunch Registry

https://github.com/caleblareau/mgatk

Software python-based command line interface for processing .bam files with mitochondrial reads and generating high-quality heteroplasmy estimation from sequencing data. This package places a special emphasis on mitochondrial genotypes generated from single-cell genomics data, primarily mtscATAC-seq, but is generally applicable across other assays.

Proper citation: mgatk (RRID:SCR_021159) Copy   

•   Source: SciCrunch Registry

https://www.med.upenn.edu/cbica/captk/

Software platform for analysis of radiographic cancer images. Used as quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

Proper citation: Cancer Imaging Phenomics Toolkit (RRID:SCR_017323) Copy   

•   Source: SciCrunch Registry

https://ccsp.hms.harvard.edu/

Center includes studies for responsiveness and resistance to anti cancer drugs. Committed to training students and postdocs, promoting junior faculty and ensuring that data and software are reproducible, reliable and publicly accessible. Member of National Cancer Institute’s Cancer Systems Biology Consortium.

Proper citation: Harvard Medical School Center for Cancer Systems Pharmacology (RRID:SCR_022831) Copy   

•   Source: SciCrunch Registry

http://cancer.osu.edu/research/cancerresearch/sharedresources/ltb/Pages/index.aspx

The OSU Comprehensive Cancer Center Leukemia Tissue Bank Shared Resource (LTBSR) facilitates the successful translation of basic leukemia research to the clinical setting via an extensive repository of tissue samples and accompanying pathologic, cytogenetic and clinical data for ready correlation of clinical and biological results. The LTBSR, which is an NCI-sponsored biorepository, has more than 40,000 vials of cryopreserved viable cells and 13,000 vials of matched frozen plasma and/or serum samples from more than 4,000 patients treated for leukemia and other malignancies. Committed to furthering translational research efforts for OSUCCC - James members and the cancer research community, the LTBSR provides investigators with training and technical support as well as procurement, processing, storage, retrieval and distribution of clinical research materials. In many cases, the LTBSR serves as the central processing lab for multi-site trials in which the principal investigator is an OSUCCC - James member. The LTBSR's goals are to: * Provide a central collection, processing and a state-of-the-art repository for samples collected from leukemia patients treated on OSUCCC - James protocols, and * Provide materials to investigators involved in collaborative studies with OSU, who examine relevant cellular and molecular properties of leukemia and correlate these properties with clinical or population-based outcomes.

Proper citation: Ohio State Leukemia Tissue Bank (RRID:SCR_000529) Copy   

•   Source: SciCrunch Registry

http://www.cpctr.net/

THIS RESOURCE IS NO LONGER IN SERVICE. Doumented on September 23,2022. The National Cancer Institute initially established the Cooperative Prostate Cancer Tissue Resource (CPCTR) to provide prostate cancer tissue samples with clinical annotation to researchers. The Resource provides access to formalin-fixed, paraffin-embedded primary prostate cancer tissue with associated clinical and follow-up data for research studies, particularly studies focused on translating basic research findings into clinical application. Fresh-frozen tissue is also available with limited clinical follow up information since these are more recent cases. The Resource database contains pathologic and clinical information linked to a large collection of prostate tissue specimens that is available for research. Researchers can determine whether the Resource has the tissues and patient data they need for their individual research studies. Consultation and interpretive services: Assistance is available from trained CPCTR pathologists. The CPCTR can provide consultative assistance in staining interpretation, and scoring, on a collaborative basis. Fresh Frozen and Paraffin Tissue: The resource has over 7,000 annotated cases (including 7,635 specimens and 38,399 annotated blocks). Tissue Microarrays (TMA): The CPCTR has slides from prostate cancer TMAs with associated clinical data. The information provided for each case on the arrays (derived from radical prostatectomy specimens) includes: age at diagnosis, race, PSA at diagnosis, tumor size, TNM stage, Gleason score and grade, and vital status and other variables.

Proper citation: CPCTR: Cooperative Prostate Cancer Tissue Resource (RRID:SCR_000803) Copy   

•   Source: SciCrunch Registry

https://www.sbpdiscovery.org/biomedical-research/shared-resources/cancer-metabolism

Facility that aims to investigate role of metabolism in cancer on cellular and organismal level, combining in vitro and in vivo analysis. Comprehensive metabolic facility, including stable isotope tracing and flux analysis. Provides analysis of metabolism using specialist instruments and methodologies, together with advice on planning experiments and analyzing data. All instruments and approaches are available on fee-for-service basis to Cancer Center scientists, but alsoto outside users. Samples for most services can be shipped, but Seahorse analyzer users need to be local so they can bring live cells for analysis.

Proper citation: Sanford Burnham Prebys Medical Discovery Institute Cancer Metabolism Core Facility (RRID:SCR_014873) Copy   

•   Source: SciCrunch Registry

http://www.salk.edu/science/core-facilities/integrative-genomics-and-bioinformatics-core/

Core facility established to assist the Salk community with integrating genomics data into their research. The primary focus of the core is to provide analysis support for next-generation sequencing applications.

Proper citation: Salk Institute Razavi Newman Integrative Genomics and Bioinformatics Core Facility (IGC) (RRID:SCR_014842) Copy   

•   Source: SciCrunch Registry

https://www.mdanderson.org/research/research-resources/core-facilities/functional-proteomics-rppa-core.html

Core provides technology for functional proteomics studies and centralized, standardized and quality controlled services locally and globally.

Proper citation: RPPA Core Facility (RRID:SCR_016649) Copy   

•   Source: SciCrunch Registry

https://voices.uchicago.edu/ucflow/

Core offers services in cytometry and antibody technology, helps to design projects, data acquisition, analysis and/or interpretation beyond routine practices,critical drafting and/or revision of manuscript for intellectual content purposes.

Proper citation: Chicago University Cytometry and Antibody Technology Core Facility (RRID:SCR_017760) Copy   

•   Source: SciCrunch Registry

https://www.mdanderson.org/research/research-resources/core-facilities/proteomics-facility.html

Facility provides mass spectrometry analysis of proteins. Provides access to mass spectrometry based proteomics technologies and services including Protein Identification, Molecular Weight Determination, Quantitative Protein Analysis, Post-translational Modification Analysis, LC or LC-MS Analysis, Equipment Usage, Additional Data Analysis:Bioinformatics, statistics, pathway analysis and Assistance preparing materials for manuscripts or grants.Consultations for custom assays for other MS Services are also available.

Proper citation: University of Texas MD Anderson Cancer Center Proteomics Core Facility (RRID:SCR_017731) Copy   

•   Source: SciCrunch Registry