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http://www.nitrc.org/projects/validate29/

Atlas was created from MRI scans of squirrel monkey brains. The atlas is currently comprised of multiple anatomical templates, diffusion MRI templates, and ex vivo templates. In addition, the templates are combined with histologically defined cortical labels, and diffusion tractography defined white matter labels.

Proper citation: VALiDATe29 Squirrel Monkey Brain Atlas (RRID:SCR_015542) Copy   

•   Source: SciCrunch Registry

http://www.cbioportal.org/

A portal that provides visualization, analysis and download of large-scale cancer genomics data sets.

Proper citation: cBioPortal (RRID:SCR_014555) Copy   

•   Source: SciCrunch Registry

http://www.proteometools.org/index.php?id=home

Project for building molecular and digital tools from human proteome to facilitate biomedical research, drug discovery, personalized medicine and life science research.

Proper citation: ProteomeTools (RRID:SCR_018535) Copy   

•   Source: SciCrunch Registry

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://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases

Probabilistic atlases covering 48 cortical and 21 subcortical structural areas, derived from structural data and segmentations kindly provided by the Harvard Center for Morphometric Analysis. T1-weighted images of 21 healthy male and 16 healthy female subjects (ages 18-50) were individually segmented by the CMA using semi-automated tools developed in-house. The T1-weighted images were affine-registered to MNI152 space using FLIRT (FSL), and the transforms then applied to the individual labels. Finally, these were combined across subjects to form population probability maps for each label. Segmentations used to create these atlases were provided by: David Kennedy and Christian Haselgrove, Centre for Morphometric Analysis, Harvard; Bruce Fischl, the Martinos Center for Biomedical Imaging, MGH; Janis Breeze and Jean Frazier from the Child and Adolescent Neuropsychiatric Research Program, Cambridge Health Alliance; Larry Seidman and Jill Goldstein from the Department of Psychiatry of Harvard Medical School.

Proper citation: Harvard - Oxford Cortical Structural Atlas (RRID:SCR_001476) 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://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

https://omics.pnl.gov/software/ms-gf

Software that performs peptide identification by scoring MS/MS spectra against peptides derived from a protein sequence database.

Proper citation: MS-GF+ (RRID:SCR_015646) Copy   

•   Source: SciCrunch Registry

http://emg.nysbc.org/redmine/projects/leginon/wiki/Leginon_Homepage

System designed for automated collection of images from a transmission electron microscope.

Proper citation: Leginon (RRID:SCR_016731) Copy   

•   Source: SciCrunch Registry

https://sleepdata.org/datasets/cfs

Portal for family based study of sleep apnea. Contains data for quantifying the familial aggregation of sleep apnea. The polysomnographic (PSG) montage signals: EEG, ECG, EOG, EMG, SpO2, plethysmography, airflow (thermistor), nasal pressure, respiratory effort, position, snore.

Proper citation: Cleveland Family Study (RRID:SCR_016585) Copy   

•   Source: SciCrunch Registry

http://marrvel.org/

Web tool to search multiple public variant databases simultaneously and provide a unified interface to facilitate the search process. Used for integration of human and model organism genetic resources to facilitate functional annotation of the human genome. Used for analysis of human genes and variants by cross-disciplinary integration of records available in public databases to facilitate clinical diagnosis and basic research.

Proper citation: MARRVEL (RRID:SCR_016871) Copy   

•   Source: SciCrunch Registry

https://github.com/fmaguire/Bridger_Assembler

Software package as de novo trascriptome assembler for RNA-Seq data. Framework for de novo transcriptome assembly using RNA-seq data. Can assemble all transcripts from short reads without using reference. Input RNA-Seq reads in fasta or fastq format, and ouput all assembled candidate transcripts in fasta format. Operating system Unix/Linux.

Proper citation: Bridger (RRID:SCR_017039) Copy   

•   Source: SciCrunch Registry

https://modbase.compbio.ucsf.edu/foxs/

Web server for computing theoretical scattering profile of structure and fitting of experimental profile. Computes SAXS profile of given atomistic model and fits it to experimental profile. Used for structural modeling applications with small angle X-ray scattering data.

Proper citation: FoXS (RRID:SCR_017269) Copy   

•   Source: SciCrunch Registry

http://www.kojak-ms.org/

Software tool for identification of cross-linked peptides from mass spectra. Used for analysis of chemically cross-linked protein complexes. Used to analyze both novel and existing data sets.

Proper citation: Kojak (RRID:SCR_021028) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/redcap-to-nda

Software for metadata-driven electronic data capture to export REDCap data dictionaries and data to the NIMH National Data Archive (NDA). Prepares data submissions as csv formatted spreadsheets for data dictionary spreadsheets and for data spreadsheets.

Proper citation: redcap-to-nda (RRID:SCR_016008) Copy   

•   Source: SciCrunch Registry

http://ecocyc.org/

Database for the bacterium Escherichia coli K-12 MG1655, the EcoCyc project performs literature-based curation of the entire genome, and of transcriptional regulation, transporters, and metabolic pathways. The long-term goal of the project is to describe the molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists, and for biologists who work with related microorganisms.

Proper citation: EcoCyc (RRID:SCR_002433) Copy   

•   Source: SciCrunch Registry

http://biositemaps.ncbcs.org/rds/search.html

Resource Discovery System is a web-accessible and searchable inventory of biomedical research resources. Powered by the Resource Discovery System (RDS) that includes a standards-based informatics infrastructure * Biositemaps Information Model * Biomedical Resource Ontology Extensions * Web Services distributed web-accessible inventory framework * Biositemap Resource Editor * Resource Discovery System Source code and project documentation to be made available on an open-source basis. Contributing institutions: University of Pittsburgh, University of Michigan, Stanford University, Oregon Health & Science University, University of Texas Houston. Duke University, Emory University, University of California Davis, University of California San Diego, National Institutes of Health, Inventory Resources Working Group Members

Proper citation: Resource Discovery System (RRID:SCR_005554) Copy   

•   Source: SciCrunch Registry

http://www.nbcr.net/

Biomedical technology research center that conducts, catalyzes and enables multiscale biomedical research, focusing on four key activities: 1) integrating computational, data and visualization resources in a transparent, advanced grid environment to enable better access to distributed data, computational resources, instruments and people; 2) developing and deploying advanced computational tools for modeling and simulation, data analysis, query and integration, three-dimensional image processing and interactive visualization; 3) delivering and supporting advanced grid/cyberinfrastructure for biomedical researchers; and 4) training a cadre of new researchers to have an interdisciplinary, working knowledge of computational technology relevant to biomedical scientists. NBCR enables biomedical scientists to address the challenge of integrating detailed structural measurements from diverse scales of biological organization that range from molecules to organ systems in order to gain quantitative understanding of biological function and phenotypes. Predictive multi-scale models and their driving biological research problems together address issues in modeling of sub-cellular biophysics, building molecular modeling tools to accelerate discovery, and defining tools for patient-specific multi-scale modeling. NBCR furthers these driving problems by developing tools and models based on rapid advances in mathematics and information technology, incorporating them into NBCR pipelines or problem solving environments, and addressing the inevitable changes in the underlying cyber-infrastructure technologies and continually adapting codes over time. Their technology focus integrates both the biological applications and the underlying support software into reproducible science workflows that can function across a number of physical infrastructures.

Proper citation: National Biomedical Computation Resource (RRID:SCR_002656) Copy   

•   Source: SciCrunch Registry

http://ncmir.ucsd.edu/

Biomedical technology research center that develops computer-aided, advanced microscopy for the acquisition of structural and functional data in the dimensional range of 1 nm to 100 um, a range encompassing macromolecules, subcellular structures and cells. Novel specimen-staining methods, imaging instrumentsincluding intermediate high-voltage transmission electron microscopes (IVEMs) and high-speed, large-format laser-scanning light microscopesand computational capabilities are available for addressing mesoscale biological microscopy of proteins and macromolecular complexes in their cellular and tissue environments. These technologies are developed to bridge understanding of biological systems between the gross anatomical and molecular scales and to make these technologies broadly available to biomedical researchers. NCMIR provides expertise, infrastructure, technological development, and an environment in which new information about the 3D ultrastructure of tissues, cells, and macromolecular complexes may be accurately and easily obtained and analyzed. NCMIR fulfills its mission through technology development, collaboration, service, training, and dissemination. It aims to develop preparative methods and analytical approaches to 3D microscopy applicable to neurobiology and cell biology, incorporating equipment and implementing software that expand the analysis of 3D structure. The core research activities in the areas of specimen development, instrument development, and software infrastructures maximize the advantages of higher voltage electron microscopy and correlated light microscopies to make ambitious imaging studies across scales routine, and to facilitate the use of resources by biomedical researchers. NCMIR actively recruits outside users who will not only make use of these resources, but who also will drive technology development and receive training.

Proper citation: National Center for Microscopy and Imaging Research (RRID:SCR_002655) Copy   

•   Source: SciCrunch Registry

https://www.bio.aps.anl.gov/

Biomedical technology research center and training resource for the study of the structure of partially ordered biological molecules, complexes of biomolecules and cellular structures under conditions similar to those present in living cells and tissues. The goal of research at BioCAT is to determine the detailed structure and mechanism of action of biological systems at the molecular level. The techniques used are X-ray fiber diffraction, X-ray solution scattering and X-ray micro-emission and micro-absorption spectroscopy, with an emphasis on time-resolved studies and the development of novel techniques.

Proper citation: BioCAT (RRID:SCR_001440) Copy   

•   Source: SciCrunch Registry