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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://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

http://www.uniprot.org/

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

Proper citation: UniProt (RRID:SCR_002380) 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

https://github.com/QTIM-Lab/DeepNeuro

Software Python package for neuroimaging data. Framework to design and train neural network architectures. Used in medical imaging community to ensure consistent performance of networks across variable users, institutions, and scanners.

Proper citation: DeepNeuro (RRID:SCR_016911) 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://github.com/plaisier-lab/sygnal

Software pipeline to integrate correlative, causal and mechanistic inference approaches into unified framework that systematically infers causal flow of information from mutations to TFs and miRNAs to perturbed gene expression patterns across patients. Used to decipher transcriptional regulatory networks from multi-omic and clinical patient data. Applicable for integrating genomic and transcriptomic measurements from human cohorts.

Proper citation: SYGNAL (RRID:SCR_023080) Copy   

•   Source: SciCrunch Registry

https://github.com/qianli10000/mtradeR

Software R package implements Joint model with Matching and Regularization and simulation pipeline. Used to test association between taxa and disease risk, and adjusted for correlated taxa screened by pre-selection procedure in abundance and prevalence, individually.

Proper citation: mtradeR (RRID:SCR_022977) Copy   

•   Source: SciCrunch Registry

https://github.com/Shenhav-and-Korem-labs/SCRuB

Software R package to help researchers address common issue of contamination in microbial studies. Well aware MiSeq decontamination program.

Proper citation: SCRuB (RRID:SCR_023518) Copy   

•   Source: SciCrunch Registry

https://github.com/genome/bam-readcount

Software tool that runs on BAM or CRAM file and generates low level information about sequencing data at specific nucleotide positions. Its outputs include observed bases, readcounts, summarized mapping and base qualities, strandedness information, mismatch counts, and position within the reads.

Proper citation: bam readcount (RRID:SCR_023653) Copy   

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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

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