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http://ccb.loni.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 31, 2022. Center focused on the development of computational biological atlases of different populations, subjects, modalities, and spatio-temporal scales with 3 types of resources: (1) Stand-alone computational software tools (image and volume processing, analysis, visualization, graphical workflow environments). (2) Infrastructure Resources (Databases, computational Grid, services). (3) Web-services (web-accessible resources for processing, validation and exploration of multimodal/multichannel data including clinical data, imaging data, genetics data and phenotypic data). The CCB develops novel mathematical, computational, and engineering approaches to map biological form and function in health and disease. CCB computational tools integrate neuroimaging, genetic, clinical, and other relevant data to enable the detailed exploration of distinct spatial and temporal biological characteristics. Generalizable mathematical approaches are developed and deployed using Grid computing to create practical biological atlases that describe spatiotemporal change in biological systems. The efforts of CCB make possible discovery-oriented science and the accumulation of new biological knowledge. The Center has been divided into cores organized as follows: - Core 1 is focused on mathematical and computational research. Core 2 is involved in the development of tools to be used by Core 3. Core 3 is composed of the driving biological projects; Mapping Genomic Function, Mapping Biological Structure, and Mapping Brain Phenotype. - Cores 4 - 7 provide the infrastructure for joint structure within the Center as well as the development of new approaches and procedures to augment the research and development of Cores 1-3. These cores are: (4)Infrastructure and Resources, (5) Education and Training, (6) Dissemination, and (7) Administration and Management. The main focus of the CCB is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging.

Proper citation: Center for Computational Biology at UCLA (RRID:SCR_000334) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/frats/

Software for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles.

Proper citation: Functional Regression Analysis of DTI Tract Statistics (RRID:SCR_002293) Copy   

•   Source: SciCrunch Registry

http://proteogenomics.musc.edu/ma/arrayQuest.php?page=home&act=manage

A web-accessible program for the analysis of DNA microarray data. ArrayQuest is designed to apply any type of DNA microarray analysis program executable on a Linux system (i.e., Bioconductor statistical and graphical methods written in R as well as BioPerl and C++ based scripts) to DNA microarray data stored in the MUSC DNA Microarray Database, the Gene Expression Omnibus (GEO) or in a password protected private database uploaded to the center point server. ArrayQuest analyses are performed on a computer cluster.

Proper citation: ArrayQuest (RRID:SCR_010935) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/Software/Debabeler

Software to manage the conversion of imaging data from one file format and convention to another. It consists of a graphical user interface to visually program the translations, and a data translation engine to read, sort and translate the input files, and write the output files to disk. The data translation engine: (1) Reads metadata from a set of image files on disk to identify the source that produced each file; (2) Groups the image files into user-defined collections using image metadata values; (3) Translates each image file collection by reading metadata and pixel data and mapping the data into the appropriate output file format through a programmable set of connected modules. The Debabeler uses the Java Image I/O Plugin Architecture to read and write a wide variety of common medical image file formats, including ANALYZE, MINC, and most variations of DICOM.

Proper citation: LONI Debabeler (RRID:SCR_001160) Copy   

•   Source: SciCrunch Registry

https://github.com/SciKnowEngine/kefed.io

Knowledge engineering software for reasoning with scientific observations and interpretations. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a "neural connection matrix" interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. The KEfED model is designed to provide a lightweight representation for scientific knowledge that is (a) generalizable, (b) a suitable target for text-mining approaches, (c) relatively semantically simple, and (d) is based on the way that scientist plan experiments and should therefore be intuitively understandable to non-computational bench scientists. The basic idea of the KEfED model is that scientific observations tend to have a common design: there is a significant difference between measurements of some dependent variable under conditions specified by two (or more) values of some independent variable.

Proper citation: Knowledge Engineering from Experimental Design (RRID:SCR_001238) Copy   

•   Source: SciCrunch Registry

http://fit.genomics.lbl.gov/cgi-bin/myFrontPage.cgi

Web tool for browsing genome wide fitness experiments for diverse bacteria from Deutschbauer lab, the Arkin lab, and collaborators. Collection of mutant phenotypes for bacterial genes of unknown function.

Proper citation: Fitness Browser (RRID:SCR_018981) Copy   

•   Source: SciCrunch Registry

http://mus.well.ox.ac.uk/gscandb/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database / display tool of genome scans, with a web interface that lets the user view the data. It does not perform any analyses - these must be done by other software, and the results uploaded into it. The basic features of GSCANDB are: * Parallel viewing of scans for multiple phenotypes. * Parallel analyses of the same scan data. * Genome-wide views of genome scans * Chromosomal region views, with zooming * Gene and SNP Annotation is shown at high zoom levels * Haplotype block structure viewing * The positions of known Trait Loci can be overlayed and queried. * Links to Ensembl, MGI, NCBI, UCSC and other genome data browsers. In GSCANDB, a genome scan has a wide definition, including not only the usual statistical genetic measures of association between genetic variation at a series of loci and variation in a phenotype, but any quantitative measure that varies along the genome. This includes for example competitive genome hybridization data and some kinds of gene expression measurements.

Proper citation: WTCHG Genome Scan Viewer (RRID:SCR_001635) Copy   

•   Source: SciCrunch Registry

http://www.isi.edu/projects/bioscholar/overview

Knowledge management and engineering system software for experimental biomedical scientists permitting a single scientific worker (at the level of a graduate student or postdoctoral worker) to design, construct and manage a shared knowledge repository for a research group derived on a local store of PDF files. Usability is especially emphasized within a laboratory so that this software could provide support to experimental scientists attempting to construct a personalized representation of their own knowledge on a medium scale. The BioScholar system uses a graphical interface to create experimental designs based on the experimental variables in the system. The design is then analyzed to construct a tabular input form based on the data flow. They call this methodology "Knowledge Engineering from Experimental Design" or "KEfED". The approach is domain-independent but domain-specific modules reasoning can be constructed to generate interpretations from the observational data represented in the KEfED model. The application is available for download as platform-specific installers including Linux, Unix, Mac OS, and Windows. The installer will install an application that will run the BioScholar server. This server uses Jetty as its integrated web server.

Proper citation: Bioscholar (RRID:SCR_001380) Copy   

•   Source: SciCrunch Registry

http://rlbl.chem.upenn.edu

Biomedical technology research center and training resource that develops time-resolved laser technologies and instrumentation, with a focus on 2-D IR spectroscopy. The technologies enable atomic-level measurements of the fastest steps in biological processes to elucidate structure and dynamics in biological macromolecules, assemblies and cells. The Center makes most of its instrumentation available for service research projects to outside users nation-wide.

Proper citation: Ultrafast Optical Processes Laboratory (RRID:SCR_006582) Copy   

•   Source: SciCrunch Registry

http://www.nrcam.uchc.edu/

Biomedical technology research center that develops new technologies for modeling cell biological processes. The technologies are integrated through Virtual Cell, a problem-solving environment built on a central database and disseminated as a Web application for the analysis, modeling and simulation of cell biological processes. NRCAM resides at the Center for Cell Analysis and Modeling, CCAM, and provides a vast array of laboratory equipment that can be used for obtaining experimental data needed to create and enhance Virtual Cell models. Microscopy instrumentation includes three confocal laser scanning microscopes including UV excitation, nonlinear optical microscopy utilizing a titanium sapphire pulsed laser, confocal-based fluorescence correlation spectroscopy, wide-field imaging workstation with cooled CCD and rapid excitation filter wheel, and dual-wavelength spectrofluorometer. Access to the facilities and technical staff is open to all researchers., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NRCAM (RRID:SCR_006134) Copy   

•   Source: SciCrunch Registry

https://www.colorado.edu/facility/ems/

Core provides access to instruments including:FEI Tecnai 12 Spirit TEM, FEI Tecnai F20 (200kV) FEG-TEM 200kV FEG-TEM,Gatan US4000 4k x 4k CCD, bottom-mount,CryoTEM and electron tomography,High-resolution TEM;FEI Tecnai F20 (200kV) FEG-TEM,300kV FEG-TEM,Gatan US4000 4k x 4k CCD, bottom-mount,CryoTEM and electron tomography,High-resolution TEM,FEI/Phillips CM100 (100kV) TEM,100kV, tungsten TEM,2k x 2k AMT CCD, bottom-mount.

Proper citation: Colorado University at Boulder EM Services Core Facility (RRID:SCR_001432) Copy   

•   Source: SciCrunch Registry

http://ohsu.eagle-i.net/i/0000012e-5e3a-7084-4106-535b80000000

In cooperation with the Data and Clinical Cores at the Layton Center, the Biomarkers and Genetics Core generates and maintains biomarker data for select biomarkers which have established roles in the characterization of subjects with or at risk of dementia. Biological markers of brain aging, dementia risk, and neurodegeneration have the potential to accelerate the identification of disease mechanisms and treatment strategies. Biomarkers may include genes, proteins, or other metabolites, and may be identified in DNA, cerebrospinal fluid (CSF), or plasma. Apolipoprotein E (APOE) genotype is generated for all research subjects. Sub-groups of subjects have other types of biomarker data. Many subjects have had genome-wide SNP data generated. In order to foster collaborative research as well as expand resources and expertise, samples (DNA, CSF, and plasma) and data are distributed to qualified investigators worldwide. Most of these researchers are pursuing the causes and modifiers of dementia. Data and samples are collected from well characterized research subjects including the healthy elderly and dementia patients.

Proper citation: Layton Alzheimers Disease Center Biomarkers and Genetics Core Lab (RRID:SCR_009911) Copy   

•   Source: SciCrunch Registry

http://ohsu.eagle-i.net/i/0000012e-5e56-c3be-4106-535b80000000

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on December 6th,2022. The Oregon Alzheimer?s Disease Center?s (OADC) Clinical Core program, directed by Dr. Jeffrey Kaye, performs longitudinal studies of the natural history of brain aging and Alzheimer''s disease in patients and healthy control volunteers. These studies which are performed through standardized neurological, neuro-psychological, and brain-imaging assessments are carried out in the Alzheimer''s Disease and Memory Assessment Clinics as well as through community-based assessments conducted in the homes of study volunteers. The Layton Center Neuroimaging Lab conducts brain-imaging MRI brain scans to assist in diagnosis of brain disease. Typically, MRI images are taken from three different planes. These planes are known as the coronal plane, sagittal plane and the axial plane. Each series of MRI images is named after the plane from which they were obtained. The Clinical Core?s research is focused on preclinical and early Alzheimer?s disease (AD) yet is also poised to participate in other relevant new research as it arises. The OADC Clinical Core recruits, assesses and follows individuals from population groups at high risk for dementia such as: the healthy ?oldest old?, subjects with family history of AD, and subjects with Mild Cognitive Impairment (MCI). Research with underserved populations The Oregon Alzheimer?s Disease Center also maintains two Satellite programs to enhance understanding of underserved populations: The Klamath Exceptional Aging Project (KEAP) is a community-based study of brain aging being conducted in Klamath Falls. The African American Dementia and Aging Project (AADAPt) s a Portland-based cohort of 100 African American seniors.

Proper citation: Layton Alzheimers Disease Center Clinical Core (RRID:SCR_009912) Copy   

•   Source: SciCrunch Registry

https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST

Software model based segmentation and registration tool. Used for segmentation of sub-cortical structures. Introduces basic segmentation and vertex analysis for detecting group differences.

Proper citation: FMRIB’s Integrated Registration and Segmentation Tool (RRID:SCR_024921) Copy   

•   Source: SciCrunch Registry

https://dna-analysis.yale.edu/

Core supports DNA Sequencing of PCR, Plasmid, BAC and Fosmid templates, Fragment Analysis of Microsatellites, AFLP, t-RFLP, SHAPE Experiments and Human Cell Line Authentication.

Proper citation: Yale University DNA Analysis on Science Hill Core Facility (RRID:SCR_017689) Copy   

•   Source: SciCrunch Registry

https://flowcore.hsc.wvu.edu/

Facility provides instrumentation and scientific support for single cell analysis and sorting. Routinely performs analysis of both eukaryotic and prokaryotic cells for expression of intracellular and extracellular proteins, cell cycle, cell proliferation, cytokine production, and cell sorting based on expression of cell surface antigen(s) and/or expression of genetically engineered intercellular fluorescent proteins.

Proper citation: West Virginia University Flow Cytometry and Single Cell Core Facility (RRID:SCR_017738) Copy   

•   Source: SciCrunch Registry

http://facs.stanford.edu/

Provides flow cytometry instrumentation and expertise. Provides operator assisted analyzer and sorter use, as well as training and support for user instrument operation.

Proper citation: Stanford University Shared FACS Core Facility (RRID:SCR_017788) Copy   

•   Source: SciCrunch Registry

http://massspec.chem.wisc.edu

Core provides mass spectrometers including Thermo Q Exactive Plus,Bruker impact II ,Bruker microflex LRF,Bruker ULTRAFLEX III,Shimadzu GCMS-QP2010S,Waters Acquity LCMS.

Proper citation: University of Wisconsin-Madison Chemistry Instrumentation Center - Mass Spectrometry Core Facility (RRID:SCR_017931) Copy   

•   Source: SciCrunch Registry

https://www.brown.edu/research/facilities/proteomics/

Core provides instrumentation and proteomics expertise to Brown University and Rhode Island-EPSCoR scientific communities and training in emerging proteomic techniques. Mass Spectrometry proteomics resources and services are provided by COBRE Center for Cancer Research Development (CCRD) at Rhode Island Hospital: Proteomics Core.

Proper citation: Brown University Division of Biology and Medicine Proteomics Shared Resource Core Facility (RRID:SCR_017910) Copy   

•   Source: SciCrunch Registry

https://med.nyu.edu/research/scientific-cores-shared-resources/microscopy-laboratory

Core offers comprehensive light and electron microscopy technologies. Our scientists use light microscopes and electron microscopes at resolutions ranging from centimeters to angstroms, providing clear and detailed images.We assist at every stage of your experiment, offering research-design consultation and instrument training, as well as guidance in study execution, analysis, and presentation for publication.

Proper citation: New York University School of Medicine Langone Health Microscopy Laboratory Core Facility (RRID:SCR_017934) Copy   

•   Source: SciCrunch Registry