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http://www.lipidmaps.org/data/structure/

Collection of structures and annotations of biologically relevant lipids that contains unique lipid structures. Structures of lipids from : LIPID MAPS Consortium's core laboratories and partners; lipids identified by LIPID MAPS experiments; biologically relevant lipids manually curated from LIPID BANK, LIPIDAT, Lipid Library, Cyberlipids, ChEBI and other public sources; novel lipids submitted to peer-reviewed journals; and computationally generated structures for appropriate classes. All the lipid structures adhere to the structure drawing rules proposed by the LIPID MAPS consortium. A number of structure viewing options are offered: gif image (default), Chemdraw (requires Chemdraw ActiveX/Plugin), MarvinView (Java applet) and JMol (Java applet). All lipids have been classified using the LIPID MAPS Lipid Classification System. Each lipid structure has been assigned a LIPID MAPS ID (LM_ID) which reflects its position in the classification hierarchy. In addition to a classification-based retrieval of lipids, users can search using either text-based or structure-based search options.

Proper citation: LIPID MAPS Structure Database (RRID:SCR_003817) Copy   

•   Source: SciCrunch Registry

http://integromedb.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. Search engine that integrates over 100 curated and publicly contributed data sources and provides integrated views on the genomic, proteomic, transcriptomic, genetic and functional information currently available. Information featured in the database includes gene function, orthologies, gene expression, pathways and protein-protein interactions, mutations and SNPs, disease relationships, related drugs and compounds.

Proper citation: IntegromeDB (RRID:SCR_004620) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/profunc/index.html

The ProFunc server had been developed to help identify the likely biochemical function of a protein from its three-dimensional structure. It uses both sequence- and structure-based methods including fold matching, residue conservation, surface cleft analysis, and functional 3D templates, to identify both the protein''''s likely active site and possible homologues in the PDB. Often, where one method fails to provide any functional insight another may be more helpful. You can submit your own structure, analyze an existing PDB entry, or retrieve the results of a previously submitted run. The files are usually stored for about 6 months before being deleted. However, they are stored on a partition that is not backed up; so, in principle, they could disappear at any time.

Proper citation: ProFunc (RRID:SCR_004450) 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://github.com/BioDepot/nbdocker

Software tool as Jupyter Notebook extension for Docker. Each Docker container encapsulates its individual computing environment to allow different programming languages and computing environments to be included in one single notebook, provides user to document code as well as computing environment.

Proper citation: nbdocker (RRID:SCR_017159) Copy   

•   Source: SciCrunch Registry

https://github.com/DReichLab/AdmixTools

Software package that supports formal tests of whether admixture occurred, and makes it possible to infer admixture proportions and dates.

Proper citation: ADMIXTOOLS (RRID:SCR_018495) Copy   

•   Source: SciCrunch Registry

https://github.com/bondarevts/flucalc

Software tool as MSS-MLE calculator for Luria–Delbrück fluctuation analysis.

Proper citation: FluCalc (RRID:SCR_019322) Copy   

•   Source: SciCrunch Registry

https://github.com/sqjin/CellChat

Software R toolkit for inference, visualization and analysis of cell-cell communication from single cell data.Quantitatively infers and analyzes intercellular communication networks from single-cell RNA-sequencing data. Predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Classifies signaling pathways and delineates conserved and context specific pathways across different datasets.

Proper citation: CellChat (RRID:SCR_021946) Copy   

•   Source: SciCrunch Registry

https://github.com/plaisier-lab/sygnal

Software pipeline to integrate correlative, causal and mechanistic inference approaches into unified framework that systematically infers causal flow of information from mutations to TFs and miRNAs to perturbed gene expression patterns across patients. Used to decipher transcriptional regulatory networks from multi-omic and clinical patient data. Applicable for integrating genomic and transcriptomic measurements from human cohorts.

Proper citation: SYGNAL (RRID:SCR_023080) Copy   

•   Source: SciCrunch Registry

https://github.com/virajbdeshpande/AmpliconArchitect

Software package designed to call circular DNA from short read WGS data.Used to identify one or more connected genomic regions which have simultaneous copy number amplification and elucidates architecture of amplicon.Used to reconstruct structure of focally amplified regions using whole genome sequencing and validate it extensively on multiple simulated and real datasets, across wide range of coverage and copy numbers.

Proper citation: AmpliconArchitect (RRID:SCR_023150) Copy   

•   Source: SciCrunch Registry

https://github.com/compbiolabucf/omicsGAN

Software generative adversarial network to integrate two omics data and their interaction network to generate one synthetic data corresponding to each omics profile that can result in better phenotype prediction. Used to capture information from interaction network as well as two omics datasets and fuse them to generate synthetic data with better predictive signals.

Proper citation: OmicsGAN (RRID:SCR_022976) Copy   

•   Source: SciCrunch Registry

http://carbonyldb.missouri.edu/CarbonylDB/index.php/

Curated data resource of protein carbonylation sites.Manually curated data resource of experimentally confirmed carbonylated proteins and sites.Provides information on other related resources such as list of other oxidative protein modification databases, list of protein oxidation and carbonylation prediction tools.

Proper citation: CarbonylDB (RRID:SCR_023924) Copy   

•   Source: SciCrunch Registry

http://nramm.scripps.edu/

Biomedical technology research center that develops, tests and applies technology aimed toward completely automating the processes involved in solving macromolecular structures using cryo-electron microscopy. The goal is to establish a resource that will serve both as a center for high-throughput molecular microscopy as well as for transferring this technique to the research community. Current Core Technology Research and Development is focused on 4 areas: improving grid substrates and specimen preparation; further automation and optimization of image acquisition; development of an integrated single particle analysis and processing pipeline; and the development of automated high throughput EM screening. NRAMM welcomes applications of both collaborative and service projects.

Proper citation: National Resource for Automated Molecular Microscopy (RRID:SCR_001448) Copy   

•   Source: SciCrunch Registry