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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://necat.chem.cornell.edu/

Biomedical technology research center for macromolecular crystallography at Sector 24 of the Advanced Photon Source at Argonne National Laboratory. The macromolecules studied by resource users often involve large unit cells, small crystals, weakly diffracting crystals and crystals with weak anomalous scattering. Technological research includes use of silicon monochromators, focusing optics, methods of phase determination, radiation damage, X-ray detectors, automated sample mounting, microdiffraction and crystallographic software.

Proper citation: Northeastern Collaborative Access Team (RRID:SCR_008999) Copy   

•   Source: SciCrunch Registry

http://www.pyrosetta.org/

Interactive Python based interface to Rosetta molecular modeling suite. Stand alone Python based implementation of Rosetta molecular modeling package that allows users to write custom structure prediction and design algorithms using major Rosetta sampling and scoring functions.

Proper citation: PyRosetta (RRID:SCR_018541) Copy   

•   Source: SciCrunch Registry

https://psbweb05.psb.ugent.be/conet/microbialnetworks/spieceasi.php

Software R package estimates inverse covariance matrix from sequencing data.Statistical method for inference of microbial ecological networks from amplicon sequencing datasets.

Proper citation: Sparse Inverse Covariance Estimation for Ecological Association Inference (RRID:SCR_022646) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/BGLR/

Software R package implements large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures.

Proper citation: Bayesian Generalized Linear Regression (RRID:SCR_022522) Copy   

•   Source: SciCrunch Registry

https://github.com/rondolab/MR-PRESSO

Software R package for performing Mendelian randomization pleiotropy residual sum and outlier method.Used to identify horizontal pleiotropic outliers in multi instrument summary level MR testing.

Proper citation: MR-PRESSO (RRID:SCR_023697) Copy   

•   Source: SciCrunch Registry

http://ncmi.bcm.edu/ncmi/

Biomedical technology research center establishing the infrastructure for fast, routine, atomic structure determination of subcellular complexes by electron cryo-microscopy, computer reconstruction and modeling. Their emphasis is on specimens that cannot currently be studied by conventional structural techniques such as x-ray crystallography or NMR. The ultimate outcome of their research is a three-dimensional image of the complex that can be used for design of drugs and vaccines for a variety of diseases. The center is focused on extending the resolution, speed and flexibility of cryo-electron microscopy for the three-dimensional structure determination of biological macromolecular assemblies. Cryo-electron microscopy can visualize molecules under near-native conditions at resolutions ranging from 0.3 to 5 nm and can yield images of individual molecules in a range of different conformations as they exist in solution. Other cryo-electron mycroscopy techniques, such as cryo-electron tomography, are being developed to capture molecular structures in situ. The equipment, techniques and expertise developed are available to the research community through collaborative projects. The NCMI also provides training through workshops and other forms of dissemination via both traditional and modern Internet-based methods.

Proper citation: National Center for Macromolecular Imaging (RRID:SCR_001445) Copy   

•   Source: SciCrunch Registry

http://www.macchess.cornell.edu/

MacCHESS Synchrotron Source for Structural Biology advances structural characterization of proteins and biomolecules critical for understanding key biological processes and properties through leveraging both established and emerging X-ray synchrotron technologies. Used to collect data that comprises all or part of research programs.

Proper citation: MacCHESS (RRID:SCR_001443) Copy   

•   Source: SciCrunch Registry

http://www.cgl.ucsf.edu/

Biomedical technology resource center that develops software and web-based resources for the visualization and analysis of molecular structure, and related data, at scales ranging from the atomic to the supramolecular. They create tools for handling and integrating diverse types of biomolecular data, including atomic-resolution coordinates, density maps, sequences, annotations, and networks. Their primary efforts are in the visualization and analysis of structures of molecules and molecular assemblies, enzyme sequence-structure-function relationships, and network representations of protein similarity, binding interactions, and biological pathways. They provide technologies to enable identifying the molecular bases of disease and phenotypic variation, annotating proteins of unknown function, identifying targets for drug development, designing drugs, and engineering proteins with new functions. RBVI distributes software tools, including the popular UCSF Chimera visualization and analysis package, develops and hosts the Structure-Function Linkage Database, and provides access to state-of-the-art computational resources in support of research projects in these areas.

Proper citation: Resource for Biocomputing Visualization and Informatics (RRID:SCR_001374) 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://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

https://github.com/BioDepot/BioDepot-workflow-builder

Software tool to create and execute reproducible bioinformatics workflows using drag and drop interface. Graphical widgets represent Docker containers executing modular task. Widgets are linked graphically to build bioinformatics workflows that can be reproducibly deployed across different local and cloud platforms. Each widget contains form-based user interface to facilitate parameter entry and console to display intermediate results.

Proper citation: BioDepot-workflow-builder (RRID:SCR_017402) Copy   

•   Source: SciCrunch Registry

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://cluspro.bu.edu/

Web tool for protein-protein docking. Server provides removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering data, and location of heparin-binding sites. Six different energy functions can be used, depending on protein type.This protocol describes use of various options, construction of auxiliary restraints files, selection of energy parameters, and analysis of results.

Proper citation: ClusPro (RRID:SCR_018248) Copy   

•   Source: SciCrunch Registry

https://github.com/Cai-Lab-at-University-of-Michigan/nTracer

Software tool as plug-in for ImageJ software. Used for tracing microscopic images.

Proper citation: nTracer (RRID:SCR_023032) Copy   

•   Source: SciCrunch Registry

https://github.com/compbiolabucf/APA-Scan

Software Python tool for detection and visualization of annotated and potential alternative polyadenylation events in downstream 3'-UTR of gene among two different biological conditions. Used for detection and visualization of 3'-UTR alternative polyadenylation with RNA-seq and 3'-end-seq data.

Proper citation: APA-Scan (RRID:SCR_022974) Copy   

•   Source: SciCrunch Registry

https://connectf.org

Software platform to integrate transcription factor gene interactions and validate regulatory networks. Gene regulatory network validation.

Proper citation: ConnecTF (RRID:SCR_022577) Copy   

•   Source: SciCrunch Registry

https://www.microbej.com/

Software tool for high throughput bacterial cell detection and quantitative analysis. Used to analyze bacterial cells. Used to process images derived from variety of microscopy experiments with special emphasis on large image sets. Performs intensity and morphology measurements as well as customized detection of poles, septa, fluorescent foci, and organelles, determines their sub-cellular localization with sub-pixel resolution, and tracks them over time.

Proper citation: MicrobeJ (RRID:SCR_023914) Copy   

•   Source: SciCrunch Registry

https://kleintools.hms.harvard.edu/tools/spring.html

Interactive web tool to visualize single cell data using force directed graph layouts. Kinetic interface for visualizing high dimensional single cell expression data. Collection of pre-processing scripts and web browser based tool for visualizing and interacting with high dimensional data.

Proper citation: SPRING (RRID:SCR_023578) Copy   

•   Source: SciCrunch Registry

https://maayanlab.cloud/kea3

Web server application that infers overrepresentation of upstream kinases whose putative substrates are in user inputted list of proteins. Used to analyze data from phosphoproteomics and proteomics studies to predict upstream kinases responsible for observed differential phosphorylations.

Proper citation: Kinase Enrichment Analysis 3 (RRID:SCR_023623) Copy   

•   Source: SciCrunch Registry