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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://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   

•   Source: SciCrunch Registry

https://oncodb.org/

Database offers integrated multi-omic data for patients across 33 cancer types. It encompasses gene expression, DNA methylation, somatic mutations, proteomic profiles, and chromatin accessibility, drawing from TCGA, GTEx, and CPTAC projects. Users can compare gene expression, DNA methylation, and protein levels between tumor and normal tissues, identifying differentially expressed genes and proteins, and examining gene-to-gene correlations. Provides oncogene mutation profiles and allows for survival analysis based on gene expression and methylation, linked to clinical parameters. Facilitates exploration of multi-omic correlations, such as gene expression with DNA methylation, and their variations with mutation status. Extends its analytical capabilities to include six major oncoviruses, offering insights into their impact on gene expression, methylation, and patient survival.

Proper citation: OncoDB (RRID:SCR_028340) 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://autodocksuite.scripps.edu/adt/

Software graphical user interface to help to set up which bonds will treated as rotatable in the ligand and to analyze dockings. Used for automated docking with selective receptor flexibility. Designed to predict how small molecules, such as substrates or drug candidates, bind to receptor of known 3D structure.

Proper citation: AutoDockTools (RRID:SCR_026401) Copy   

•   Source: SciCrunch Registry

https://github.com/spreka/biomagdsb

Software tool as parameter-free deep learning framework for nucleus segmentation using image style transfer. Cell segmentation tool.

Proper citation: NucleAIzer (RRID:SCR_026500) Copy   

•   Source: SciCrunch Registry

https://github.com/compgenomics/MeTPeak

Software package for finding the location of m6A sites in MeRIP-seq data.

Proper citation: MeTPeak (RRID:SCR_026533) Copy   

•   Source: SciCrunch Registry

https://github.com/agshumate/Liftoff

Software genome annotation lift-over tool capable of mapping genes between two assemblies of the same or closely related species. Aligns genes from reference genome to target genome and finds the mapping that maximizes sequence identity while preserving the structure of each exon, transcript and gene. Used for accurate mapping of gene annotations.

Proper citation: Liftoff (RRID:SCR_026535) Copy   

•   Source: SciCrunch Registry

https://endomap.hms.harvard.edu/

Structural interactome viewer. Interactive database of endosomal protein-protein interactions identified by cross-linking mass spectrometry and modeled by AlphaFold multimer. Structural protein interactome of human early endosomes.

Proper citation: EndoMap (RRID:SCR_026690) Copy   

•   Source: SciCrunch Registry

https://github.com/DerrickWood/kraken2

Software tool as second version of Kraken taxonomic sequence classification system.

Proper citation: kraken2 (RRID:SCR_026838) 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