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https://immunedb.readthedocs.io/en/latest/

Software system for storing and analyzing high throughput B and T cell immune receptor sequencing data. Comprised of web interface and of Python analysis tools to process raw reads for gene usage, infer clones, aggregate data, and run downstream analyses, or in conjunction with other AIRR tools using its import and export features.

Proper citation: ImmuneDB (RRID:SCR_017125) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6301786/

Device to control spatial and temporal variations in oxygen tensions to better replicate in vivo biology. Consists of three parallel connected tissue chambers and oxygen scavenger channel placed adjacent to these tissue chambers. Provides consistent control of spatial and temporal oxygen gradients in tissue microenvironment and can be used to investigate important oxygen dependent biological processes present in cancer, ischemic heart disease, and wound healing.

Proper citation: Microfluidic device to attain high spatial and temporal control of oxygen (RRID:SCR_017131) Copy   

•   Source: SciCrunch Registry

https://exrna-atlas.org

Software tool as data and metadata repository of Extracellular RNA Communication Consortium. Atlas includes small RNA sequencing and qPCR derived exRNA profiles from human and mouse biofluids. All RNAseq datasets are processed using version 4 of exceRpt small RNAseq pipeline. Atlas accepts submissions for RNAseq or qPCR data.

Proper citation: exRNA Atlas (RRID:SCR_017221) Copy   

•   Source: SciCrunch Registry

http://www.genepattern-notebook.org/

Interactive analysis notebook environment that streamlines genomics research by interleaving text, multimedia, and executable code into unified, sharable, reproducible “research narratives.” It integrates the dynamic capabilities of notebook systems with an investigator-focused, simple interface that provides access to hundreds of genomic tools without the need to write code.

Proper citation: GenePattern Notebook (RRID:SCR_015699) Copy   

•   Source: SciCrunch Registry

http://tissues.jensenlab.org

Database that integrates evidence on tissue expression from manually curated literature, proteomics and transcriptomics screens, and automatic text mining. It maps all evidence to common protein identifiers and Brenda Tissue Ontology terms, and further unifies it by assigning confidence scores that facilitate comparison of the different types and sources of evidence.

Proper citation: TISSUES (RRID:SCR_015665) Copy   

•   Source: SciCrunch Registry

http://amp.pharm.mssm.edu/LJP/

Interactive on line tool where signatures are tagged with user selected metadata and external transcript signatures are projected onto network. Browser to visualize signatures from breast cancer cell lines treated with single molecule perturbations.

Proper citation: LINCS Joint Project - Breast Cancer Network Browser (RRID:SCR_016181) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/group/exonarray/cgi-bin/plot_selector.pl

Transcriptome database of acutely isolated purified astrocytes, neurons, and oligodendrocytes. Provides improved cell-type-specific markers for better understanding of neural development, function, and disease.

Proper citation: Exon Array Browser (RRID:SCR_008712) Copy   

•   Source: SciCrunch Registry

http://www.rhesusbase.org/drugDisc/CAM.jsp

OKCAM (Ontology-based Knowledgebase for Cell Adhesion Molecules) is an online resource for human genes known or predicted to be related to the processes of cell adhesion. These genes include members of the cadherin, immunoglobulin/FibronectinIII (IgFn), integrin, neurexin, neuroligin, and catenin families. Totally 496 human CAM genes were compiled and annotated. We have mapped these genes onto a novel cell adhesion molecule ontology (CAMO) that provides a hierarchical description of cell adhesion molecules and their functions. It is intended to provide a means to facilitate better and better understanding of the global and specific properties of CAMs through their genomic features, regulatory modes, expression patterns and disease associations become clearer. You may browse by CAM ontology, Chromosomes and Full Gene list.

Proper citation: OKCAM: Ontology-based Knowledgebase for Cell Adhesion Molecules (RRID:SCR_010696) Copy   

•   Source: SciCrunch Registry

https://genome-cancer.ucsc.edu/

A suite of web-based tools to visualize, integrate and analyze cancer genomics and its associated clinical data. It is possible to display your own clinical data within one of their datasets.

Proper citation: UCSC Cancer Genomics Browser (RRID:SCR_011796) Copy   

•   Source: SciCrunch Registry

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

A software tool for annotating biomedical investigations and the resulting data, then producing a MAGE-TAB file. This software is a standalone desktop which features: an editor function, an annotation modifier, incorporation of terms from biomedical ontologies, standard templates for common experiment types, a design aid to help create a new document, and a validator that checks for syntactic and semantic violations.

Proper citation: Annotare (RRID:SCR_000319) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/release/bioc/html/nondetects.html

Software R package to model and impute non-detects in results of qPCR experiments.Used to directly model non-detects as missing data.

Proper citation: nondetects (RRID:SCR_001702) Copy   

•   Source: SciCrunch Registry

http://www.pathology.med.ohio-state.edu/HTRN/apc/default.asp

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. The Adenoma Polyp Tissue Bank (APTB) receives whole blood from patients enrolled in the Prevention of Sporadic Colorectal Adenomas with Celecoxib clinical trial. We have reached our accrual on blood submissions, so we will no longer be receiving blood specimens The objectives of this trial are as follows: A. To determine the efficacy and safety of celecoxib versus placebo in preventing the occurrence of newly detected colorectal adenomas in subjects at increased risk for colorectal carcinoma. In addition to incidence, other established risk factors will be evaluated for their association with occurrence of new colorectal adenomas, including cancer family history and adenoma size, histopathologic grade, multiplicity and location. Primary assessment of treatment efficacy will be the reduction in the number of subjects with adenomas at colonoscopy after Year 1 and Year 3 of study drug use. Secondary assessments of treatment efficacy will be 1) the number of adenomas 2) the histopathologic grade of adenomas and 3) the size of adenomas, also measured after one year and three years of study drug use. These factors will be incorporated into a risk model for predicting adenoma occurrence and response to celecoxib. B. To determine the efficacy of celecoxib versus placebo in modulating one or more of a panel of biomarkers for colorectal cancer at the cellular and molecular level sampled in a subset of subjects at selective sites at baseline and after Year 1 and Year 3 of study drug use. These biomarkers will include measurements of aberrant crypt foci (ACF), proliferation (index and crypt distribution), apoptosis (index and crypt distribution), COX expression and activity. If modulation of one or more mucosal biomarkers occur, we will explore whether it correlates with the development of incident colorectal neoplasia (adenomas/carcinomas), thereby attempting to validate the surrogacy of that biomarker. C. To develop a specimen bank. Serum and white blood cells are isolated from whole blood and adenoma tissue blocks and slides are banked. Banked specimens will become available for use in correlative science studies at a later point. This project began in 1999 and will be extended through 2006. The lead principal investigator is Monica M. Bertagnolli, MD, Brigham and Women''s Hospital, Boston, MA, and the APTB Director is Scott Jewell, Ph.D., Department of Pathology, The Ohio State University. The APTB is supported by the NIH, NCI Division of Cancer Prevention, in connection with the Strang Cancer Prevention Center, Cornell University, New York., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Adenoma Polyp Tissue Bank (RRID:SCR_005366) 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://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://github.com/humanlongevity/HLA

Software tool for fast and accurate HLA typing from short read sequence data. Iteratively refines mapping results at amino acid level to achieve four digit typing accuracy for both class I and II HLA genes, taking only 3 min to process 30× whole genome BAM file on desktop computer.

Proper citation: xHLA (RRID:SCR_022277) Copy   

•   Source: SciCrunch Registry

https://github.com/RabadanLab/arcasHLA

Software tool for high resolution HLA typing from RNAseq. Fast and accurate in silico inference of HLA genotypes from RNA-seq.

Proper citation: arcasHLA (RRID:SCR_022286) 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://www.karmanos.org/

Center for patient care, education and research on cancer. The institute focuses its research on prevention methods, early detection, treatment and finding cures.

Proper citation: Karmanos Cancer Institute (RRID:SCR_000508) Copy   

•   Source: SciCrunch Registry

http://discover.nci.nih.gov/gominer/

GoMiner is a tool for biological interpretation of "omic" data including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question, What does it all mean biologically? To answer this question, GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories. The insights gained through GoMiner can generate hypotheses to guide additional research. GoMiner displays the genes within the framework of the Gene Ontology hierarchy in two ways: * In the form of a tree, similar to that in AmiGO * In the form of a "Directed Acyclic Graph" (DAG) The program also provides: * Quantitative and statistical analysis * Seamless integration with important public databases GoMiner uses the databases provided by the GO Consortium. These databases combine information from a number of different consortium participants, include information from many different organisms and data sources, and are referenced using a variety of different gene product identification approaches.

Proper citation: GoMiner (RRID:SCR_002360) Copy   

•   Source: SciCrunch Registry

http://dctd.cancer.gov/

Division of NCI that takes prospective cancer detection and treatment leads, facilitates their paths to clinical application, and expedites the initial and subsequent large-scale testing of new agents, biomarkers, imaging tests, and other therapeutic interventions (radiation, surgery, immunotherapy) in patients. DCTD, like all of NCI, supports many programs that could not be done without government funding - investigators supported by the division engage in scientifically sound, high-risk research that may yield great benefits for patients with cancer, but are too difficult or risky for industry or academia to pursue. This includes a particular emphasis on the development of distinct molecular signatures for cancer, refined molecular assays, and state-of-the-art imaging techniques that will guide oncologic therapy in the future. The division has eight major programs that work together to bring unique molecules, diagnostic tests, and therapeutic interventions from the laboratory bench to the patient bedside: * Cancer Diagnosis Program * Cancer Imaging Program * Cancer Therapy Evaluation Program * Developmental Therapeutics Program * Radiation Research Program * Translational Research Program * Biometrics Research Branch * Office of Cancer Complementary and Alternative Medicine

Proper citation: DCTD (RRID:SCR_004196) Copy   

•   Source: SciCrunch Registry