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https://biocars.uchicago.edu/

Biomedical technology research center and training resource that is a state-of-the art, national user facility for synchrotron-based studies of dynamic and static properties of macromolecules by X-ray scattering techniques such as crystallography (specializing in time-resolved), small- and wide-angle X-ray scattering and fiber diffraction. BioCARS operates two X-ray beamlines, embedded in a Biosafety Level 3 (BSL-3) facility unique in the U.S. that permits safe studies of biohazardous materials such as human pathogens., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BioCARS (RRID:SCR_001439) Copy   

•   Source: SciCrunch Registry

http://nrnb.org/index.html

Biomedical technology research center that develops new algorithms, visualizations and conceptual frameworks to study biological networks at multiple levels and scales, from protein-protein and genetic interactions to cell-cell communication and vast social networks. They are developing freely available, open-source suite of software technology that broadly enables network-based visualization, analysis, and biomedical discovery for NIH-funded researchers. This software is enabling researchers to assemble large-scale biological data into models of networks and pathways and to use these networks to better understand how biological systems operate under normal conditions and how they fail in disease. The National Resource for Network Biology is organized around the following key components: Technology Research and Development, Driving Biomedical Projects, Outreach, Training and Dissemination of Tools. The NRNB supports several types of training events, including both virtual and live workshops; tutorials sessions for clinicians, biologists and bioinformaticians; presentations and demonstrations at conferences; online tutorials and webcasts; and annual symposium.

Proper citation: National Resource for Network Biology (RRID:SCR_004259) Copy   

•   Source: SciCrunch Registry

http://depts.washington.edu/yeastrc/

Biomedical technology research center that (1) exploits the budding yeast Saccharomyces cerevisiae to develop novel technologies for investigating and characterizing protein function and protein structure (2) facilitates research and extension of new technologies through collaboration, and (3) actively disseminates data and technology to the research community. Through collaboration, the YRC freely provides resources and expertise in six core technology areas: Protein Tandem Mass Spectrometry, Protein Sequence-Function Relationships, Quantitative Phenotyping, Protein Structure Prediction and Design, Fluorescence Microscopy, Computational Biology.

Proper citation: Yeast Resource Center (RRID:SCR_007942) Copy   

•   Source: SciCrunch Registry

http://snyderome.stanford.edu/

Data set generated by personal omics profiling of Dr. Michael Snyder at Stanford University. It combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. The analysis revealed various medical risks, including type II diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions.

Proper citation: iPOP (RRID:SCR_008991) Copy   

•   Source: SciCrunch Registry

http://proteomics.ucsd.edu/

Biomedical technology research center that focuses on the computational bottlenecks that impair the interpretation of data, bringing modern algorithmic approaches to mass spectrometry and building a new generation of reliable, open-access software tools to support both new mass spectrometry instrumentation and emerging applications.

Proper citation: Center for Computational Mass Spectrometry (RRID:SCR_008161) Copy   

•   Source: SciCrunch Registry

http://msr.dom.wustl.edu/

Biomedical technology research center that develops mass spectrometry-based tools for the study of proteins, lipids and metaboilites. These include biomarker identification, stable isotope mass spectrometry and the analysis of intact proteins. Our goals are: * to conduct basic research in the science of mass spectrometry * to establish collaborative research projects with scientists at WU and at other institutions * to provide a service in mass spectrometry * to educate and train students in mass spectrometry * to disseminate results of our research and descriptions of the subject of mass spectrometry

Proper citation: NIH / NCRR Mass Spectrometry Resource Washington University in St. Louis (RRID:SCR_009009) Copy   

•   Source: SciCrunch Registry

http://glycotech.ccrc.uga.edu/

Biomedical technology research center that develops technologies to increase understanding of the molecular basis of the involvement of carbohydrates in protein-carbohydrate interactions in disease and to develop more powerful technologies necessary to achieve this goal. Complex carbohydrates play an important role in many biomedically important processes, including inflammatory response, hormone action, malignancy, viral and bacterial infections and cell differentiation. The resource combines complimentary technologies: synthetic chemistry, nuclear magnetic resonance, mass spectrometry, computational biology, protein expression and cell-based assays. As new technologies are developed, application to these processes will be pursued through collaborative and service projects.

Proper citation: Resource for Integrated Glycotechnology (RRID:SCR_009008) Copy   

•   Source: SciCrunch Registry

https://bioams.llnl.gov/

Biomedical technology research center that develops and refines accelerator mass spectrometry methods and instrumentation for the precise, quantitative and cost-effective measurement of the effects of drugs and toxicants on humans at safe doses. It facilitates the use of accelerator mass spectrometry in biomedical research and provides training and access for researchers.

Proper citation: National Resource for Biomedical Accelerator Mass Spectrometry (RRID:SCR_009006) Copy   

•   Source: SciCrunch Registry

http://cell.ccrc.uga.edu/world/glycomics/glycomics.php

Biomedical technology research center that develops and implements new technologies to investigate the glycome of cells, including glycoproteomics and glycoconjugate analysis, transcript analysis and bioinformatics. It develops the tools and technology to analyze in detail the glycoprotein and glycolipid expression of mouse embryonic stem cells and the cells into which they differentiate. The technology developed in the Center will allow an understanding of how glycosylation is controlled during differentiation and will allow the development of tools to promote the use of stem cells to treat human disease. In addition, the technology developed will be applicable to the study of other cell types, including cancer cells that are progressing to a more invasive phenotype. The technology developed will also allow others in the scientific community to participate in glycomics research through dissemination of the new methods developed and through the analytical services provided by the resource to other scientists requesting assistance in glycomic analyses.

Proper citation: Integrated Technology Resource for Biomedical Glycomics (RRID:SCR_009003) Copy   

•   Source: SciCrunch Registry

http://www-ssrl.slac.stanford.edu/content/science/ssrl-smb-program

Biomedical technology research center that operates as a integrated center with three primary areas (or cores) of technological research and development and scientific focus: macromolecular crystallography (MC), X-ray absorption spectroscopy (XAS) and small-angle X-ray scattering/diffraction (SAXS) . Central to the core technological developments in all three areas is the development and utilization of improved detectors and instrumentation, especially to be able to take maximum advantage of the high brightness of SSRL?s third-generation synchrotron X-ray storage ring (SPEAR3). A primary focus is the use of enhanced computing and data management tools to provide more user-friendly, real-time and on-line instrumentation control, including full remote access for crystallography, data reduction and analysis.

Proper citation: SSRL Structural Molecular Biology (RRID:SCR_009000) Copy   

•   Source: SciCrunch Registry

https://pycontact.github.io/

Software tool for analysis of non-covalent interactions in molecular dynamics trajectories. Implemented in Python and is universally applicable to any kind of MD trajectory supported by MDAnalysis package.

Proper citation: PyContact (RRID:SCR_025066) Copy   

•   Source: SciCrunch Registry

https://www.npatlas.org

Open access knowledge base for microbial natural products discovery. Database of microbially derived natural product structures. Provides coverage of bacterial and fungal natural products to visualize chemical diversity. Includes compounds and contains referenced data for structure, compound names, source organisms, isolation references, total syntheses, and instances of structural reassignment. Interactive web portal permits searching by structure, substructure, and physical properties. Provides mechanisms for visualizing natural products chemical space and dashboards for displaying author and discovery timeline data. Atlas has been developed under FAIR principles.

Proper citation: Natural Products Atlas (RRID:SCR_025107) Copy   

•   Source: SciCrunch Registry

https://compbio.uth.edu/FusionGDB2/

Functional annotation database of human fusion genes.FusionGDB 2.0 has updates of contents such as up-to-date human fusion genes, fusion gene breakage tendency score with FusionAI deep learning model based on 20 kb DNA sequence around BP, investigation of overlapping between fusion breakpoints with human genomic features across cellular role's categories, transcribed chimeric sequence and following open reading frame analysis with coding potential based on deep learning approach with Ribo-seq read features, and rigorous investigation of protein feature retention of individual fusion partner genes in protein level.

Proper citation: FusionGDB2 (RRID:SCR_025299) Copy   

•   Source: SciCrunch Registry

https://polymerscreen.yale.edu

Open access web app that allows users to search for optimal condition for extraction of membrane proteins into membrane active polymers which allows for retention of native membrane environment around target protein.

Proper citation: MAP Database Guide for Membrane Protein Solubilization (RRID:SCR_025656) Copy   

•   Source: SciCrunch Registry

https://github.com/zhouhj1994/LinDA

Software linear models for differential abundance analysis of microbiome compositional data. Used to tackle compositional effects in differential abundance analysis. It fits linear regression models on centered log2-ratio transformed data, identifies bias term due to transformation and compositional effect, and corrects bias using mode of regression coefficients. It could fit mixed-effect models.

Proper citation: LinDA (RRID:SCR_025966) Copy   

•   Source: SciCrunch Registry

https://pypi.org/project/SpaGCN/

Software graph convolutional network to integrate gene expression and histology to identify spatial domains and spatially variable genes. SpaGCN integrates information from gene.

Proper citation: SpaGCN (RRID:SCR_025978) Copy   

•   Source: SciCrunch Registry

https://github.com/YingMa0107/CARD/

Software R package for spatial transcriptomics. Deconvolution method that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations.

Proper citation: Conditional AutoRegressive Deconvolution (RRID:SCR_026310) Copy   

•   Source: SciCrunch Registry

https://web.uri.edu/riinbre/mic/

Core provides sequencing and bioinformatics support for INBRE and non-INBRE researchers. Provides data science services adjacent to traditional bioinformatics; access to computational and software resources for INBRE network institutions, particularly primarily undergraduate institutions; training for students and faculty in data science methods. Maintains professional network with other core and user facilities in Rhode Island and beyond to maximize resources available to our users.Utilizes novel technologies such as virtual/augmented reality for use in teaching and research.

Proper citation: Rhode Island INBRE Molecular Informatics Core Facility (RRID:SCR_017685) Copy   

•   Source: SciCrunch Registry

https://core.bioinformatics.udel.edu

CBCB Bioinformatics Data Science Core builds upon infrastructure of the Center for Bioinformatics and Computational Biology. Delaware Biotechnology Institute , Delaware Data Science Institute, and Protein Information Resource. Core helps with planning your research or analyzing your current data. Services:Consultation and Collaboration, Proposal Assistance,Training, Bioinformatics Software, Computational Hardware, Database and Web Services, Next Gen Analysis, Custom Analysis.

Proper citation: University of Delaware CBCB Bioinformatics Data Science Core Facility (RRID:SCR_017696) Copy   

•   Source: SciCrunch Registry

http://www.uwyo.edu/microscopy/

Core to assist researchers and students in their imaging needs of fluorescence and electron microscopy and to increase use of microscopes in science education. Consultation, training or touring facility is also available by appointment.

Proper citation: Wyoming University Jenkins Microscopy Core Facility (RRID:SCR_017758) Copy   

•   Source: SciCrunch Registry