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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://mygene.info/

Web service for querying or retrieving gene annotation data.

Proper citation: MyGene.info (RRID:SCR_018660) Copy   

•   Source: SciCrunch Registry

http://www.genenetwork.org/

Web platform that provides access to data and tools to study complex networks of genes, molecules, and higher order gene function and phenotypes. Sequence data (SNPs) and transcriptome data sets (expression genetic or eQTL data sets). Quantitative trait locus (QTL) mapping module that is built into GN is optimized for fast on-line analysis of traits that are controlled by combinations of gene variants and environmental factors. Used to study humans, mice (BXD, AXB, LXS, etc.), rats (HXB), Drosophila, and plant species (barley and Arabidopsis). Users are welcome to enter their own private data.

Proper citation: GeneNetwork (RRID:SCR_002388) Copy   

•   Source: SciCrunch Registry

https://github.com/PhysiCell-Tools/PhysiCell-Studio

Software graphical tool to allow easy editing of (XML) model, create initial positions of cells, run simulation, and visualize results. To contribute, fork and make PRs to the development branch. Used to create, execute, and visualize multicellular model using PhysiCell.

Proper citation: PhysiCell Studio (RRID:SCR_025311) Copy   

•   Source: SciCrunch Registry

https://github.com/phillipnicol/scGBM

Software application for model-based dimensionality reduction of scRNA-seq data. Quantifies uncertainty in each cell's latent position and leverages these uncertainties to assess confidence associated with given cell clustering. On real and simulated single-cell data produces low-dimensional embeddings that better capture relevant biological information while removing unwanted variation. Used for model-based dimensionality reduction for single-cell RNA-seq with generalized bilinear models.

Proper citation: scGBM (RRID:SCR_025518) Copy   

•   Source: SciCrunch Registry

https://github.com/STAR-Fusion/STAR-Fusion

Software tool to leverage chimeric and discordant read alignments identified by STAR aligner to predict fusions. Component of Trinity Cancer Transcriptome Analysis Toolkit. Used to identify candidate fusion transcripts supported by Illumina reads. Maps junction reads and spanning reads to reference annotation set.

Proper citation: STAR-Fusion (RRID:SCR_025853) Copy   

•   Source: SciCrunch Registry

http://www.tarp.nih.gov/

Trans-NIH program encouraging and facilitating the study of the underlying mechanisms controlling blood vessel growth and development. Other aims include: to identify specific targets and to develop therapeutics against pathologic angiogenesis in order to reduce the morbidity due to abnormal blood vessel proliferation in a variety of disease states; to better understand the process of angiogenesis and vascularization to improve states of decreased vascularization; to encourage and facilitate the study of the processes of lymphangiogenesis; and to achieve these goals through a multidisciplinary approach, bringing together investigators with varied backgrounds and varied interests.

Proper citation: Trans-Institute Angiogenesis Research Program (RRID:SCR_000384) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. A web program that organizes lists of genes of interest (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology and automates the analysis of multiple microarrays then integrates the results across all of them in exportable output files and visualizations. High-Throughput GoMiner is an enhancement of GoMiner and is implemented with both a command line interface and a web interface. The program can also: efficiently perform automated batch processing of an arbitrary number of microarrays; produce a human- or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories; integrate the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories; provide a fast form of false discovery rate multiple comparisons calculation; and provide annotations and visualizations for relating transcription factor binding sites to genes and GO categories.

Proper citation: High-Throughput GoMiner (RRID:SCR_000173) Copy   

•   Source: SciCrunch Registry

http://www.pbtc.org/

The PEDIATRIC BRAIN TUMOR CONSORTIUM (PBTC) is a multidisciplinary cooperative research organization devoted to the study of correlative tumor biology and new therapies for primary CNS tumors of childhood. PBTC's mission is to contribute rapidly and effectively to the understanding and cure of these tumors through the conduct of multi-center, multidisciplinary, innovative studies with designs and analyses based on uniformly high quality statistical science. While the primary mission of the PBTC is to identify through laboratory and clinical science superior treatment strategies for children with brain cancers, the PBTC investigators recognize their profound responsibility to meet the special needs of the children and families as they face this enormous challenge. Members are committed to working within their institutions and communities to improve support services and follow up care for these patients and their families. The PBTC's primary objective is to rapidly conduct novel phase I and II clinical evaluations of new therapeutic drugs, new biological therapies, treatment delivery technologies and radiation treatment strategies in children from infancy to 21 years of age with primary central nervous system (CNS) tumors. A second objective is to characterize reliable markers and predictors (direct or surrogate) of brain tumors' responses to new therapies. The Consortium conducts research on brain tumor specimens in the laboratory to further understand the biology of pediatric brain tumors. A third objective is to develop and coordinate innovative neuro-imaging techniques. Through the PBTC's Neuro-Imaging Center, formed in May 2000, research to evaluate new treatment response criteria and neuro-imaging methods to understand regional brain effects is in progress. These imaging techniques can also advance understanding of significant neuro-toxicity in a developing child's central nervous system. The Neuro-Imaging Center is supported in part by private sources - grants from foundations and non-profit organizations - in addition to the NCI. As an NCI funded Consortium, the Pediatric Brain Tumor Consortium (PBTC) is required to make research data available to other investigators for use in research projects. An investigator who wishes to use individual patient data from one or more of the Consortium's completed and published studies must submit in writing a description of the research project, the PBTC studies from which data are requested, the specific data requested, and a list of investigators involved with the project and their affiliated research institutions. A copy of the requesting investigator's CV must also be provided. Participating Institutions: Children's Hospital of Philadelphia, Children's National Medical Center (Washington, DC), Children's Memorial Hospital (Chicago), Duke University, National Cancer Institute, St. Jude Children's Research Hospital, Texas Children's Cancer Center, University of California at San Francisco, and University of Pittsburgh.

Proper citation: Pediatric Brain Tumor Consortium (RRID:SCR_000658) Copy   

•   Source: SciCrunch Registry

http://www.px.nsls.bnl.gov/

Biomedical technology research center that creates optimal facilities and environments and support for macromolecular structure determination by synchrotron X-ray diffraction at the National Synchrotron Light Source for the benefit of outside and in-house investigators. The PXRR innovates new access modes such as Mail-in crystallography, builds new facilities, currently on the X25 undulator, advances automation, develops remote participation software, collaborates with outside groups, teaches novice users, and supports vising investigators with 7-day, 20-hours staff coverage.

Proper citation: Macromolecular Crystallography Research Resource (RRID:SCR_001442) Copy   

•   Source: SciCrunch Registry

http://slicer.org/

A free, open source software package for visualization and image analysis including registration, segmentation, and quantification of medical image data. Slicer provides a graphical user interface to a powerful set of tools so they can be used by end-user clinicians and researchers alike. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac Os X. Slicer is based on VTK (http://public.kitware.com/vtk) and has a modular architecture for easy addition of new functionality. It uses an XML-based file format called MRML - Medical Reality Markup Language which can be used as an interchange format among medical imaging applications. Slicer is primarily written in C++ and Tcl.

Proper citation: 3D Slicer (RRID:SCR_005619) Copy   

•   Source: SciCrunch Registry

http://www.cancerdiagnosis.nci.nih.gov/

National program to improve the diagnosis and assessment of cancer by moving scientific knowledge into clinical practice by coordinating and funding resources and research for the development of innovative in vitro diagnostics, novel diagnostic technologies and appropriate human specimens. The Cancer Diagnosis Program is divided into four branches: Biorepository and Biospecimen Research Branch (BBRB), Diagnostic Biomarkers and Technology Branch (DBTB), Diagnostics Evaluation Branch (DEB), and the Pathology Investigation and Resources Branch (PIRB).

Proper citation: CDP (RRID:SCR_004236) Copy   

•   Source: SciCrunch Registry

http://cancer.osu.edu/Pages/index.aspx

As the Midwest''s first and Ohio''s only fully dedicated cancer hospital and research institute, The Ohio State University Comprehensive Cancer CenterArthur G. James Cancer Hospital and Solove Research Institute (OSUCCC-James) is one of the nation''s premier cancer centers for the prevention, detection and treatment of cancer. The OSUCCC-James is one of only 40 centers in the United States designated by the National Cancer Institute a Comprehensive Cancer Center. In addition, the OSUCCC-James is a founding member of the National Comprehensive Cancer Network (NCCN), an alliance of 21 of the world''s leading cancer centers that develops clinical practice guidelines to improve the quality and effectiveness of care provided to patients with cancer. The Ohio State cancer program is part of The Ohio State University, the largest public university in the nation. We are affiliated with The Ohio State University Medical Center, one of the largest and most diverse academic medical centers in the nation and the only academic medical center in central Ohio. The cancer program at Ohio State encompasses more than 200 comprehensive cancer center members from 13 of the 18 colleges at The Ohio State University and includes physicians from 16 specialties. The OSUCCCJames'' singular focus on cancer has led to multiple accomplishments that have changed the standards of care with respect to prevention, diagnosis and treatment, in a way that substantially improves outcomes for cancer patients.

Proper citation: OSUCCC-James (RRID:SCR_004790) Copy   

•   Source: SciCrunch Registry

http://pdbml.pdb.org/

Markup Language that provides a representation of PDB data in XML format. The description of this format is provided in XML schema of the PDB Exchange Data Dictionary. This schema is produced by direct translation of the mmCIF format PDB Exchange Data Dictionary Other data dictionaries used by the PDB have been electronically translated into XML/XSD schemas and these are also presented in the list below. * PDBML data files are provided in three forms: ** fully marked-up files, ** files without atom records ** files with a more space efficient encoding of atom records * Data files in PDBML format can be downloaded from the RCSB PDB website or by ftp. * Software tools for manipulating PDB data in XML format are available.

Proper citation: Protein Data Bank Markup Language (RRID:SCR_005085) 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