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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/ccipd/MRQy

Software quality assurance and checking tool for quantitative assessment of magnetic resonance imaging and computed tomography data. Used for quality control of MR imaging data.

Proper citation: MRQy (RRID:SCR_025779) Copy   

•   Source: SciCrunch Registry

https://spatialge.moffitt.org/

Web application, a user friendly, point-and-click implementation of spatialGE R package. Contains collection of methods for visualization and spatial statistics analysis of tissue microenvironment and heterogeneity using spatial transcriptomics experiments. Used for user-friendly analysis of spatial transcriptomics data.

Proper citation: Moffitt spatialGE (RRID:SCR_025980) Copy   

•   Source: SciCrunch Registry

https://github.com/kbolton-lab/ArCH

Software somatic variant calling pipeline designed to detect low variant allele fraction clonal hematopoiesjsonsis variants.

Proper citation: ArCH (RRID:SCR_025975) 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

https://www.bioconductor.org/packages/release/bioc/html/methylSig.html

Software R package as whole genome DNA methylation analysis pipeline. Used for testing differentially methylated cytosines or regions in whole-genome bisulfite sequencing or reduced representation bisulfite sequencing experiments. Several options exist for either site-specific or sliding window tests, and variance estimation.

Proper citation: MethylSig (RRID:SCR_025849) Copy   

•   Source: SciCrunch Registry

https://cbc.app.vumc.org/tnbc/

Website for predicting the subtype of triple negative breast cancer sample based on its gene expression profile.

Proper citation: TNBCtype (RRID:SCR_026238) 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

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

https://github.com/higlass/higlass

Web-based visual exploration and analysis of genome interaction maps.

Proper citation: HiGlass (RRID:SCR_026687) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/apeglm.html

Software package provides Bayesian shrinkage estimators for effect sizes for variety of GLM models, using approximation of posterior for individual coefficients.

Proper citation: apeglm (RRID:SCR_026951) Copy   

•   Source: SciCrunch Registry

https://github.com/mskilab-org/JaBbA

Software tool to infer junction-balanced genome graphs with high fidelity. Builds genome graph based on junctions and read depth from whole genome sequencing, inferring optimal copy numbers for both vertices (DNA segments) and edges (bonds between segments).

Proper citation: JaBba (RRID:SCR_027134) Copy   

•   Source: SciCrunch Registry

https://www.bioconductor.org/packages/release/bioc/html/sesame.html

Software R package for reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions.

Proper citation: SeSAMe (RRID:SCR_027388) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/sivic/

Software framework and application suite for processing and visualization of DICOM MR Spectroscopy data. Through the use of DICOM, SIVIC aims to facilitate the application of MRS in medical imaging studies.

Proper citation: Spectroscopic Imaging, VIsualization, and Computing (SIVIC) (RRID:SCR_027875) Copy   

•   Source: SciCrunch Registry

http://itools.loni.usc.edu/

An infrastructure for managing of diverse computational biology resources - data, software tools and web-services. The iTools design, implementation and meta-data content reflect the broad NCBC needs and expertise (www.NCBCs.org).

Proper citation: iTools (RRID:SCR_009626) Copy   

•   Source: SciCrunch Registry

http://homer.ucsd.edu/

Software tools for Motif Discovery and next-gen sequencing analysis. Used for analyzing ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and numerous other types of functional genomics sequencing data sets. Collection of command line programs for unix style operating systems written in Perl and C++.

Proper citation: HOMER (RRID:SCR_010881) 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