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http://avis.princeton.edu/pixie/index.php

bioPIXIE is a general system for discovery of biological networks through integration of diverse genome-wide functional data. This novel system for biological data integration and visualization, allows you to discover interaction networks and pathways in which your gene(s) (e.g. BNI1, YFL039C) of interest participate. The system is based on a Bayesian algorithm for identification of biological networks based on integrated diverse genomic data. To start using bioPIXIE, enter your genes of interest into the search box. You can use ORF names or aliases. If you enter multiple genes, they can be separated by commas or returns. Press ''submit''. bioPIXIE uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the bioPIXIE algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. As you move the mouse over genes in the network, interactions involving these genes are highlighted. If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed. You may need to download the Adobe Scalable Vector Graphic (SVG) plugin to utilize the visualization tool (you will be prompted if you need it).

Proper citation: bioPIXIE (RRID:SCR_004182) Copy   

•   Source: SciCrunch Registry

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://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://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://www2.mrc-lmb.cam.ac.uk/groups/murshudov/content/balbes/balbes_layout.html

Software system for solving protein structures using x-ray crystallographic data. Automatic molecular replacement pipeline for molecular replacement in CCP4. Integrates into one system all components necessary for solving crystal structure by Molecular Replacement. System is automated so that it needs no user intervention when running combination of jobs such as model searching, molecular replacement and refinement.

Proper citation: BALBES (RRID:SCR_018763) Copy   

•   Source: SciCrunch Registry

https://github.com/RabadanLab/arcasHLA

Software tool for high resolution HLA typing from RNAseq. Fast and accurate in silico inference of HLA genotypes from RNA-seq.

Proper citation: arcasHLA (RRID:SCR_022286) Copy   

•   Source: SciCrunch Registry

https://www.utsouthwestern.edu/labs/danuser/software/

Software package as quantitative image analysis software for measurement of microtubule dynamics. MATLAB software for tracking full dynamics of microtubules based on plusTIP marker live cell image sequences.

Proper citation: plusTipTracker (RRID:SCR_021890) Copy   

•   Source: SciCrunch Registry

http://wikipathways.org/

Open and collaborative platform dedicated to curation of biological pathways. Each pathway has dedicated wiki page, displaying current diagram, description, references, download options, version history, and component gene and protein lists. Database of biological pathways maintained by and for scientific community.

Proper citation: WikiPathways (RRID:SCR_002134) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/friaptamerstream/

The Aptamer Database is a comprehensive, annotated repository for information about aptamers and in vitro selection. This resource is provided to collect, organize and distribute all the known information regarding aptamer selection. Aptamers are DNA or RNA molecules that have been selected from random pools based on their ability to bind other molecules. Aptamers have been selected which bind nucleic acid, proteins, small organic compounds, and even entire organisms.

Proper citation: Aptamer Database - The Ellington Lab (RRID:SCR_001781) Copy   

•   Source: SciCrunch Registry

http://www.mirtoolsgallery.org/miRToolsGallery/node/1055

Comprehensive resource of microRNA target predictions and expression profiles. Used for whole genome prediction of miRNA target genes. For each miRNA, target genes are selected on basis of sequence complementarity using position weighted local alignment algorithm, free energies of RNA-RNA duplexes, and conservation of target sites in related genomes. Provides information about set of genes potentially regulated by particular microRNA, co-occurrence of predicted target sites for multiple microRNAs in mRNA and microRNA expression profiles in tissues. Users are allowed to customize algorithm, numerical parameters, and position-specific rules., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: miRanda (RRID:SCR_017496) Copy   

•   Source: SciCrunch Registry

https://prokoplab.com/vistedd/

Database of SARS-CoV-2 and other viruses. Integrates structural and dynamic insights with viral evolution for proteins coded by virus. Each virus within database has workflow performed on each protein. Workflow consists of protein modeling, molecular dynamic simulations, evolutionary analysis, and mapping of protein-protein interactions. On page for each protein is link to individual protein data folder system, video of protein rotating with conservation, details of protein function, widget to purchase 3D print of protein at cost of production, amino acid movement from molecular dynamic simulations, and table of data for each amino acid of protein.

Proper citation: Viral Integrated Structural Evolution Dynamic Database (RRID:SCR_018793) Copy   

•   Source: SciCrunch Registry

http://cisbp.ccbr.utoronto.ca

Software tool as catalog of inferred sequence binding preferences. Online library of transcription factors and their DNA binding motifs.

Proper citation: CIS-BP (RRID:SCR_017236) Copy   

•   Source: SciCrunch Registry

http://www.mrmatlas.org/

Resource of targeted proteomics assays to detect and quantify proteins in complex proteome digests by mass spectrometry. Used to quantify the complete human proteome.

Proper citation: SRMAtlas (RRID:SCR_016996) 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