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https://github.com/cytoscape/RCy3

Software R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort.Network biology using Cytoscape from within R.

Proper citation: RCy3 (RRID:SCR_027668) Copy   

•   Source: SciCrunch Registry

https://weghornlab.org/software.html

Software tool which derives gene-specific probabilistic estimates of the strength of negative and positive selection in cancer.

Proper citation: CBaSE (RRID:SCR_027765) Copy   

•   Source: SciCrunch Registry

https://github.com/vanallenlab/comut

Software Python library for creating comutation plots to visualize genomic and phenotypic information. Used for visualizing genomic and phenotypic information via comutation plots.

Proper citation: CoMUT (RRID:SCR_027745) Copy   

•   Source: SciCrunch Registry

https://github.com/SalasLab/HiTIMED

Software DNA methylation-based algorithm, to estimate cell proportions in tumor microenvironment. Profiles tumor, immune, and angiogenic components, allowing researchers to study tumor composition and its clinical implications using archival biospecimens.

Proper citation: HiTIMED (RRID:SCR_028180) Copy   

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http://rankprop.gs.washington.edu/svm-fold/

This web server makes predictions of family, superfamily and fold level classifications of proteins based on the Structural Classification of Proteins (SCOP) hierarchy using the Support Vector Machine (SVM) learning algorithm. SVM-FOLD detects subtle protein sequence similarities by learning from all available annotated proteins, as well as utilizing potential hits as identified by PSI-BLAST. Predictions of classes of proteins that do not have any known example with a significant pairwise PSI-BLAST E-value can still be found using SVMs.

Proper citation: SVM-fold: Protein Fold Prediction (RRID:SCR_006834) Copy   

•   Source: SciCrunch Registry

http://compgen.bscb.cornell.edu/phast/

A freely available software package for comparative and evolutionary genomics that consists of about half a dozen major programs, plus more than a dozen utilities for manipulating sequence alignments, phylogenetic trees, and genomic annotations. For the most part, PHAST focuses on two kinds of applications: the identification of novel functional elements, including protein-coding exons and evolutionarily conserved sequences; and statistical phylogenetic modeling, including estimation of model parameters, detection of signatures of selection, and reconstruction of ancestral sequences. It consists of over 60,000 lines of C code.

Proper citation: PHAST (RRID:SCR_003204) Copy   

•   Source: SciCrunch Registry

http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:Jackal

Jackal is a collection of programs designed for the modeling and analysis of protein structures. Its core program is a versatile homology modeling package. It contains twelve individual programs, each with their own function.

Proper citation: Jackal (RRID:SCR_008665) Copy   

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http://fatcat.burnham.org/

Web server for flexible protein structure comparison. Structure alignment is formulated as the aligned fragment pairs chaining process allowing at most t twists, and the flexible structure alignment is transformed into a rigid structure alignment when t is forced to be 0., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: FATCAT (RRID:SCR_014631) Copy   

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http://sherlock.ucsf.edu/

Service to discover disease genes in GWAS using eQTL signature matching by simply submitting your list of GWAS associations (SNPs and p-values). It is important to upload all SNPs in your association study, not just the top hits. Sherlock may be able to group multiple lower-confidence SNPs to discover functionally-important genes.

Proper citation: Sherlock (RRID:SCR_001628) Copy   

•   Source: SciCrunch Registry

http://www.dbmi.pitt.edu/nlpfront

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. Repository of de-identified clinical reports available for NLP researchers has been designed. Work with the AMIA NLP working group in designing annotation schemas and obtaining annotations, design a repository for shareable annotations, help design and execute a shared task in IE from clinical reports. The University of Pittsburgh NLP Repository contains clinical reports that are available to the community for NLP research purposes and comprises: # Report Repository - one month of de-identified clinical reports from multiple hospitals and # Annotation Repository - annotations performed on reports from the Report Repository. Anyone performing annotations on reports from the NLP Repository is required to deposit their annotations. The Repository contains reports of the following types generated from multiple hospitals during a single month: * History and Physicals * Progress Notes * Consultation Reports * Radiology Reports * Surgical Pathology Reports * Emergency Department Reports * Discharge Summaries * Operative Reports * Cardiology Reports

Proper citation: Open Clinical Report Repository (RRID:SCR_013585) Copy   

•   Source: SciCrunch Registry

https://www.nature.com/articles/s41467-018-03367-w

Nanodroplet processing platform for deep and quantitative proteome profiling of 10 to 100 mammalian cells. It enhances efficiency and recovery of sample processing by downscaling processing volumes.

Proper citation: nanoPOTS (RRID:SCR_017129) Copy   

•   Source: SciCrunch Registry

http://publications.nigms.nih.gov/insidelifescience/

The NIGMS Inside Life Science series brings you inside the science of health. Each story shows how basic biomedical researchfrom the history of a field to the people doing cutting-edge work todaylays the foundation for advances in disease diagnosis, treatment and prevention. Through explorations of how the body works and highlights from recent studies, you''ll discover even more on what scientists have found and are finding about fundamental life processes. NIGMS supported all of the featured research.

Proper citation: NIGMS Inside Life Science (RRID:SCR_005852) Copy   

•   Source: SciCrunch Registry

https://maayanlab.cloud/chea3/

Web based transcription factor enrichment analysis. Web server ranks TFs associated with user-submitted gene sets. ChEA3 background database contains collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate composite rank that improves prediction of correct upstream TF compared to ranks produced by individual libraries.

Proper citation: ChIP-X Enrichment Analysis 3 (RRID:SCR_023159) Copy   

•   Source: SciCrunch Registry

https://rmats.sourceforge.io

Software tool to detect differential alternative splicing events from RNA-Seq data. Calculates P-value and false discovery rate that difference in isoform ratio of gene between two conditions exceeds given user-defined threshold. From RNA-Seq data can automatically detect and analyze alternative splicing events corresponding to all major types of alternative splicing patterns. Handles replicate RNA-Seq data from both paired and unpaired study design.

Proper citation: rMATS (RRID:SCR_023485) Copy   

•   Source: SciCrunch Registry

http://dockground.bioinformatics.ku.edu/

Data sets, tools and computational techniques for modeling of protein interactions, including docking benchmarks, docking decoys and docking templates. Adequate computational techniques for modeling of protein interactions are important because of the growing number of known protein 3D structures, particularly in the context of structural genomics. The first release of the DOCKGROUND resource (Douguet et al., Bioinformatics 2006; 22:2612-2618) implemented a comprehensive database of cocrystallized (bound) protein-protein complexes in a relational database of annotated structures. Additional releases added features to the set of bound structures, such as regularly updated downloadable datasets: automatically generated nonredundant set, built according to most common criteria, and a manually curated set that includes only biological nonobligate complexes along with a number of additional useful characteristics. Also included are unbound (experimental and simulated) protein-protein complexes. Complexes from the bound dataset are used to identify crystallized unbound analogs. If such analogs do not exist, the unbound structures are simulated by rotamer library optimization. Thus, the database contains comprehensive sets of complexes suitable for large scale benchmarking of docking algorithms. Advanced methodologies for simulating unbound conformations are being explored for the next release. The Dockground project is developed by the Vakser lab at the Center for Bioinformatics at the University of Kansas. Parts of Dockground were co-developed by Dominique Douguet from the Center of Structural Biochemistry (INSERM U554 - CNRS UMR5048), Montpellier, France.

Proper citation: Dockground: Benchmarks, Docoys, Templates, and other knowledge resources for DOCKING (RRID:SCR_007412) Copy   

•   Source: SciCrunch Registry

http://bowtie-bio.sourceforge.net/recount/

RNA-seq gene count datasets built using the raw data from 18 different studies. The raw sequencing data (.fastq files) were processed with Myrna to obtain tables of counts for each gene. For ease of statistical analysis, they combined each count table with sample phenotype data to form an R object of class ExpressionSet. The count tables, ExpressionSets, and phenotype tables are ready to use and freely available. By taking care of several preprocessing steps and combining many datasets into one easily-accessible website, we make finding and analyzing RNA-seq data considerably more straightforward.

Proper citation: ReCount - A multi-experiment resource of analysis-ready RNA-seq gene count datasets (RRID:SCR_001774) Copy   

•   Source: SciCrunch Registry

http://mus.well.ox.ac.uk/mouse/INBREDS/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2025. Data set of genotypes available for 480 strains and 13370 successful SNP assays that are mapped to build34 of the mouse genome, including 107 SNPs that are mapped to random unanchored sequence 13374 SNPs are mapped onto Build 33 of the mouse genome. You can access the data relative to Build 33 or Build 34.

Proper citation: Wellcome-CTC Mouse Strain SNP Genotype Set (RRID:SCR_003216) Copy   

•   Source: SciCrunch Registry

http://pir.georgetown.edu/pro/

An ontological representation of protein-related entities, explicitly defining them and showing the relationships between them. Each PRO term represents a distinct class of entities (including specific modified forms, orthologous isoforms, and protein complexes) ranging from the taxon-neutral to the taxon-specific. PRO encompasses three sub-ontologies: proteins based on evolutionary relatedness (ProEvo); protein forms produced from a given gene locus (ProForm); and protein-containing complexes (ProComp).

Proper citation: PRO (RRID:SCR_002902) Copy   

•   Source: SciCrunch Registry

https://maria.stanford.edu/index.php

Web multimodal recurrent neural network tool designed to predict HLA-II (Human Leukocyte Antigen class II) peptide ligand presentation. It uses cell HLA alleles, peptide sequences, and source genes to evaluate antigen presentation. Used for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles.

Proper citation: MARIA (RRID:SCR_028673) Copy   

•   Source: SciCrunch Registry

https://discover.nci.nih.gov/rsconnect/cellminercdb/

Web application integrating cancer cell line pharmacogenomics. Enables exploration and analysis of cancer cell line pharmacogenomic data across different sources. Focuses on cancer patient-derived human cell line molecular and pharmacological data. CellMinerCDB (v1.2) includes several improvements.

Proper citation: CellMinerCDB (RRID:SCR_025649) Copy   

•   Source: SciCrunch Registry