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https://ccb.jhu.edu/software/stringtie/

Software application for assembling of RNA-Seq alignments into potential transcripts. It enables improved reconstruction of a transcriptome from RNA-seq reads. This transcript assembling and quantification program is implemented in C++ .

Proper citation: StringTie (RRID:SCR_016323) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/DESeq2.html

Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.

Proper citation: DESeq2 (RRID:SCR_015687) Copy   

•   Source: SciCrunch Registry

https://github.com/KrishnaswamyLab/MAGIC

Software tool for imputing missing values restoring structure of large biological datasets.Method that shares information across similar cells, via data diffusion, to denoise cell count matrix and fill in missing transcripts.

Proper citation: Markov Affinity based Graph Imputation of Cells (RRID:SCR_022371) Copy   

•   Source: SciCrunch Registry

https://www.rdocumentation.org/packages/DGCA/versions/1.0.2

Software R package to perform differential gene correlation analysis. Performs differential correlation analysis on input matrices, with multiple conditions specified by design matrix.

Proper citation: Differential Gene Correlation Analysis (RRID:SCR_020964) Copy   

•   Source: SciCrunch Registry

https://github.com/labsyspharm/cylinter

Open source software tool as interactive image segmentation filter for multiplex microscopy that aids in identification and removal of cell segmentation instances corrupted by optical and image processing artifacts.

Proper citation: CyLinter (RRID:SCR_021157) Copy   

•   Source: SciCrunch Registry

https://github.com/JamieHeather/stitchr

Software Python tool for stitching coding T cell receptors nucleotide sequences from V,J,CDR3 information. Produces complete coding sequences representing fully spliced TCR cDNA given minimal V,J,CDR3 information.

Proper citation: Stitchr (RRID:SCR_022139) Copy   

•   Source: SciCrunch Registry

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

Software written in R for determining cluster count and membership by stability evidence in unsupervised analysis. Provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset with item tracking, item consensus and cluster consensus plots.

Proper citation: ConsensusClusterPlus (RRID:SCR_016954) Copy   

•   Source: SciCrunch Registry

https://github.com/qiicr/dcmqi

Software library to help with the conversion between imaging research formats and the standard DICOM representation for image analysis results. Used to implement conversion of the data stored in commonly used research formats into the standard DICOM representation. Available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer.

Proper citation: dcmqi (RRID:SCR_016933) Copy   

•   Source: SciCrunch Registry

http://marrvel.org/

Web tool to search multiple public variant databases simultaneously and provide a unified interface to facilitate the search process. Used for integration of human and model organism genetic resources to facilitate functional annotation of the human genome. Used for analysis of human genes and variants by cross-disciplinary integration of records available in public databases to facilitate clinical diagnosis and basic research.

Proper citation: MARRVEL (RRID:SCR_016871) Copy   

•   Source: SciCrunch Registry

https://github.com/hakyimlab/PrediXcan

Software tool to detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations. Used to test the molecular mechanisms through which genetic variation affects phenotype.

Proper citation: PrediXcan (RRID:SCR_016739) Copy   

•   Source: SciCrunch Registry

https://github.com/yarden/MISO/blob/fastmiso/docs/source/sashimi.rst

Software tool for quantitative visualization of aligned RNA-Seq reads that enables quantitative comparison of exon usage across samples or experimental conditions.

Proper citation: Sashimiplot (RRID:SCR_016861) Copy   

•   Source: SciCrunch Registry

https://github.com/BlaisProteomics/mzStudio

Software tool for proteomics data analysis, visualization, and notebook application. Dynamic digital canvas for user driven interrogation of mass spectrometry data. Operating system Unix/Linux, Windows.

Proper citation: mzStudio (RRID:SCR_017088) Copy   

•   Source: SciCrunch Registry

https://combine-lab.github.io/salmon/

Software tool for quantifying expression of transcripts using RNA-seq data. Provides fast and bias-aware quantification of transcript expression. Transcriptome-wide quantifier to correct for fragment GC-content bias.

Proper citation: Salmon (RRID:SCR_017036) Copy   

•   Source: SciCrunch Registry

https://amp.pharm.mssm.edu/geneshot/

Software tool as search engine for ranking genes from arbitrary text queries. Enables to enter arbitrary search terms, to receive ranked lists of genes relevant to search terms. Returned ranked gene lists contain genes that were previously published in association with search terms, as well as genes predicted to be associated with terms based on data integration from multiple sources. Search results are presented with interactive visualizations.

Proper citation: Geneshot (RRID:SCR_017582) Copy   

•   Source: SciCrunch Registry

http://taylor0.biology.ucla.edu/structureHarvester/

Web based program for collating results generated by program STRUCTURE. Provides assess and visualize likelihood values across multiple values of K and hundreds of iterations for easier detection of number of genetic groups that best fit data. Reformats data for use in downstream programs, such as CLUMPP.It is complement for using software Structure in genetics population. Website and program for visualizing STRUCTURE output and implementing Evanno method., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Structure Harvester (RRID:SCR_017636) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/ipd/

A set of specialist databases related to the study of polymorphic genes in the immune system. The IPD project works with specialist groups or nomenclature committees who provide and curate individual sections before they are submitted to IPD for online publication. The IPD project stores all the data in a set of related databases. IPD currently consists of four databases: * IPD-KIR, contains the allelic sequences of Killer-cell Immunoglobulin-like Receptors, * IPD-MHC, is a database of sequences of the Major Histocompatibility Complex of different species; * IPD-human platelet antigens, alloantigens expressed only on platelets and * IPD-ESTDAB, which provides access to the European Searchable Tumour cell-line database, a cell bank of immunologically characterized melanoma cell lines.

Proper citation: IPD - Immuno Polymorphism Database (RRID:SCR_003004) Copy   

•   Source: SciCrunch Registry

http://chtn.nci.nih.gov

The Cancer Diagnosis Program of the National Cancer Institute (NCI) initiated the Cooperative Human Tissue Network (CHTN) in 1987 to provide increased access to human tissue for basic and applied scientists from academia and industry to accelerate the advancement of discoveries in cancer diagnosis and treatment. This unique resource provides remnant human tissues and fluids from routine procedures to investigators who utilize human biospecimens in their research. Unlike tissue banks, the CHTN works prospectively with each investigator to tailor specimen acquisition and processing to meet their specific project requirements. Because the CHTN is funded by the NCI, the CHTN is able to maintain nominal processing fees for its services. The CHTN is comprised of five adult divisions and one pediatric division. Each of the adult divisions coordinates investigator applications/requests based upon the investigator's geographic location within North America. The Pediatric Division manages all investigators who request pediatric specimens only. The CHTN divisions share coordination for requests from outside North America. The CHTN divisions work both independently with individual investigators and together as a seamless unit to fulfill requests that are difficult to serve by any single division. The CHTN's unique informatics system allows each division to effectively communicate and network the needs of its investigators to all CHTN divisions. The Network as a whole can then help fulfill an investigator's request. Biospecimens from surgeries, autopsies and other routine procedures: Malignant, Benign, Diseased, Normal, Biofluids (urine, serum, plasma, buffy coat) High quality specimens at LOW processing fees: Fresh, Frozen, Floating in fixative, RNAlater, Paraffin embedded or and/or unstained slides, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Cooperative Human Tissue Network (RRID:SCR_004446) Copy   

•   Source: SciCrunch Registry

https://htrn.osu.edu/Services/Biorepository/Pages/default.aspx

The HTRN biospecimen bank is comprised of samples for the Ohio State University Cancer and Leukemia Group B Pathology Coordinating Office (CALGB-PCO) and the Ohio State University Midwestern Division of the Cooperative Human Tissue Network (CHTN). The CALGB-PCO banks biospecimens donated by patients enrolled in clinical trials. Samples can include tumor and normal tissue, plasma, serum, whole blood and white blood cells and urine. All of these samples are used later in correlative studies. The Midwestern Division of the CHTN stores a temporary biospecimen bank of tumor and normal tissue, tissue slides and paraffin embedded tissue blocks for research investigators throughout the country and Canada who are trying to find a cure for cancer. As part of the HTRN biospecimen bank, a Rees Scientific equipment monitoring system helps to secure the integrity and quality of samples stored in the biorepository. Scientific research within the HTRN is currently underway to determine the best methods in tissue storage for long term use. The NCI First-Generation Guidelines for NCI-Supported Biorepositories and the NCI Best Practices for Biospecimen Resources are continuously reviewed and adapted by the HTRN.

Proper citation: Ohio State Biorepository (RRID:SCR_004714) Copy   

•   Source: SciCrunch Registry

http://pilgrm.princeton.edu

PILGRM (the platform for interactive learning by genomics results mining) puts advanced supervised analysis techniques applied to enormous gene expression compendia into the hands of bench biologists. This flexible system empowers its users to answer diverse biological questions that are often outside of the scope of common databases in a data-driven manner. This capability allows domain experts to quickly and easily generate hypotheses about biological processes, tissues or diseases of interest. Specifically PILGRM helps biologists generate these hypotheses by analyzing the expression levels of known relevant genes in large compendia of microarray data. PILGRM is for the biologist with a set of proteins relevant to a disease, biological function or tissue of interest who wants to find additional players in that process. It uses a data driven method that provides added value for literature search results by mining compendia of publicly available gene expression datasets using lists of relevant and irrelevant genes (standards). PILGRM produces publication quality PDFs usable as supplementary material to describe the computational approach, standards and datasets. Each PILGRM analysis starts with an important biological question (e.g. What genes are relevant for breast cancer but not mammary tissue in general?). For PILGRM to discover relevant genes, it needs examples of both genes that you would (positive) and would not (negative) find interesting. Lists of these genes are what we call standards and in PILGRM you can build your own standards or you can use standards from common sources that we pre-load for your convenience. PILGRM lets you build your own literature-documented standards so that processes, disease, and tissues that are not well covered in databases of tissue expression, disease, or function can still be used for an analysis.

Proper citation: PILGRM (RRID:SCR_004749) Copy   

•   Source: SciCrunch Registry

http://cancer.case.edu/sharedresources/tissue/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. The Case Comprehensive Cancer Center''s Biorepository and Tissue Processing Core Facility (BTPC) serves two primary functions: 1. To build an inventory of remnant human tissues, blood and other body fluids (collectively termed biospecimens) targeted towards cancer and other medical research, for later assignment to investigators; and 2. To provide long term, controlled storage of biospecimens for specific researchers. These samples are for research purposes only and may not be used for clinical diagnosis or implantation into humans. Clinical information relating to the samples and donors are collected and maintained in a secure database. Samples and data are de-identified or de-linked before release to the researcher unless he/she has specific IRB approval to gain access to this information. Remnant biospecimens are prospectively collected from surgical procedures, autopsies and clinical laboratories for the BTPC by the Human Tissue Procurement Facility (HTPF), which operates under UH-IRB Protocol 01-02-45. Blood and bone marrow specimens are collected for the BTPC by the Hematopoietic Stem Cell Core Facility (HSCC), which operates under UH-IRB Protocol 09-90-195. The Division of Surgical Pathology at University Hospitals Case Medical Center (UHCMC) has clinical archives of paraffin blocks that can be made available through the BTPC for retrospective research studies under the approval of the Vice Chair for Clinical Affairs at UHCMC. Surgical Pathologists associated with the BTPC are responsible for determining which blocks can be made available and how much material can be removed from the blocks. Types of Tissue Available * Malignant, benign, diseased, normal and normal human tissues * Normal adjacent tissues available paired with tumor specimens in many cases * Tissues are collected from over 50 anatomic sites * Frozen specimens, OCT-embedded and paraffin-embedded tissues * Large array of paraffin-embedded specimens from clinical archives of paraffin blocks and QC research blocks maintained by the HTPF * Peripheral blood and bone marrow samples from initial visits and follow-up procedures are processed to obtain serum and cell fractions for storage * No samples are collected from individuals with known infectious illnesses * Fetal biospecimens are not collected due to state and local statutes

Proper citation: Case Comprehensive Cancer Center Biorepository and Tissue Processing Core Facility (RRID:SCR_004382) Copy   

•   Source: SciCrunch Registry