Displaying results 1 - 20 out of 10427 total results.
A fully open downloadable database of 1200 resting state fMRI (R-fMRI) datasets collected from 33 sites around the world.
The era of discovery science for human brain function was inaugurated by the collaborative launch of the Project by leading members of the functional magnetic resonance imaging (fMRI) community. Following the precedent of full unrestricted data sharing, which has become the norm in molecular genetics, the Functional Connectomes Project (FCP) entailed the aggregation and public release of over 1200 resting state fMRI (R-fMRI) datasets collected from 33 sites around the world. In just over 6 months, the release generated over 9000 downloads and ~32,000 page-views from 1,223 cities in 78 countries.
Having provided the first large-scale demonstration of the feasibility and scientific value of open sharing of R-fMRI data, the next major challenge is to make the aggregation and sharing of well-phenotyped datasets a cultural norm for the imaging community. Comprehensive phenotypic information must be made available with imaging datasets to facilitate sophisticated data-mining a process by which novel relationships between phenotypic and imaging data can be revealed. A second paradigm shift from retrospective to prospective data sharing is also necessary. That is, in contrast to the FCP release, which primarily comprised datasets that had already been published, prospective data sharing involves regularly scheduled (e.g., weekly, monthly, or quarterly) sharing of data collected at contributing sites, as the data is being collected. The notion of sharing newly acquired data, rather than waiting until those data have been published, is novel in the imaging community, but is common practice in fields such as genetics where discovery science has been successfully implemented. In order for such a shift in practice to occur, one or more imaging groups must take the lead, and set an example for the field.
# To enhance the 1000 FCP by including comprehensive phenotypic data.
# To establish a common protocol for sharing phenotypic/metadata via the 1000 FCP.
# To initiate open, prospective data-sharing for the neuroimaging community.
Database of genomic sequence data spanning several human populations including many families, that can be downloaded. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared.
Recent improvements in sequencing technology (next-gen sequencing platforms) have sharply reduced the cost of sequencing. The 1000 Genomes Project is the first project to sequence the genomes of a large number of people, to provide a comprehensive resource on human genetic variation. As with other major human genome reference projects, data from the 1000 Genomes Project will be made available quickly to the worldwide scientific community through freely accessible public databases.
The goal of the 1000 Genomes Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. This goal can be attained by sequencing many individuals lightly. To sequence a person's genome, many copies of the DNA are broken into short pieces and each piece is sequenced. The many copies of DNA mean that the DNA pieces are more-or-less randomly distributed across the genome. The pieces are then aligned to the reference sequence and joined together. To find the complete genomic sequence of one person with current sequencing platforms requires sequencing that person's DNA the equivalent of about 28 times (called 28X). If the amount of sequence done is only an average of once across the genome (1X), then much of the sequence will be missed, because some genomic locations will be covered by several pieces while others will have none. The deeper the sequencing coverage, the more of the genome will be covered at least once. Also, people are diploid; the deeper the sequencing coverage, the more likely that both chromosomes at a location will be included. In addition, deeper coverage is particularly useful for detecting structural variants, and allows sequencing errors to be corrected.
Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing.
The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via our mirrored ftp sites.
A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use.
The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes
The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year.
Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data.
All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP.
Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.
A simple, easy to navigate platform for researchers to connect with high quality research tools.
Software application for constructing 2-d crossover-based map (entry from Genetic Analysis Software)
Simple software program for calculating linkage disequilibrium (LD) measures between two polymorphic markers. (entry from Genetic Analysis Software)
Software application (entry from Genetic Analysis Software)
Software package for reconstructing three-dimensional models of brain structures from 2-D delineations using a customizable and reproducible workflow. 3dBAR also works as an on-line service (http://service.3dbar.org) offering a variety of functions for the hosted datasets:
* downloading reconstructions of desired brain structures in predefined quality levels in various supported formats as well as created using customizable settings,
* previewing models as bitmap thumbnails and (for webGL enabled browsers) interactive manipulation (zooming, rotating, etc.) of the structures,
* downloading slides from available datasets as SVG drawings.
3dBAR service can also be used by other websites or applications to enhance their functionality.
* Operating System: Linux
* Programming Language: Python
* Supported Data Format: NIfTI-1, Other Format, VRML
iPhone and iPad app that provides a good overview of the brain and its structures allowing you to rotate and zoom around 29 interactive structures with your touch screen. Discover how each brain region functions, what happens when it is injured, and how it is involved in mental illness. Each detailed structure comes with information on functions, disorders, brain damage, case studies, and links to modern research.
Compatible with iPhone, iPod touch and iPad. Requires iOS 3.0 or later.
3D DTI anatomical rat brain atlases have been created by the UNC- Chapel Hill Department of Psychiatry and the CAMID research collaboration. There are three age groups, postnatal day 5, postnatal day 14, and postnatal day 72.
The subjects were Sprague-Dawley rats that were controls in a study on cocaine abuse and development. The P5 and P14 templates were made from scans of twenty rats each (ten female, ten male); the P72, from six females. The individual cases have been resampled to isotropic resolution, manually skull-stripped, and deformably registered via an unbiased atlas building method to create a template for each age group. Each template was then manually segmented using itk-SNAP software.
Each atlas is made up of 3 files, a template image, a segmentation, and a label file.
Database of high-quality craniofacial anthropometric normative data for the research and clinical community based on digital stereophotogrammetry. Unlike traditional craniofacial normative datasets that are limited to measures obtained with handheld calipers and tape measurers, the anthropometric data provided here are based on digital stereophotogrammetry, a method of 3D surface imaging ideally suited for capturing human facial surface morphology. Also unlike more traditional normative craniofacial resources, the 3D Facial Norms Database allows users to interact with data via an intuitive graphical interface and - given proper credentials - gain access to individual-level data, allowing users to perform their own analyses.
Database containing structural annotations for the proteomes of just under 100 organisms. Using data derived from public databases of translated genomic sequences, representatives from the major branches of Life are included: Prokaryota, Eukaryota and Archaea.
The annotations stored in the database may be accessed in a number of ways. The help page provides information on how to access the database.
3D-GENOMICS is now part of a larger project, called e-Protein. The project brings together similar databases at three sites: Imperial College London , University College London and the European Bioinformatics Institute . e-Protein's mission statement is To provide a fully automated distributed pipeline for large-scale structural and functional annotation of all major proteomes via the use of cutting-edge computer GRID technologies.
The following databases are incorporated: NRprot, SCOP, ASTRAL, PFAM, Prosite, taxonomy, COG
The following eukaryotic genomes are incorporated: Anopheles gambiae, protein sequences from the mosquito genome; Arabidopsis thaliana, protein sequences from the Arabidopsis genome; Caenorhabditis briggsae, protein sequences from the C.briggsae genome; Caenorhabditis elegans protein sequences from the worm genome; Ciona intestinalis protein sequences from the sea squirt genome; Danio rerio protein sequences from the zebrafish genome; Drosophila melanogaster protein sequences from the fruitfly genome; Encephalitozoon cuniculi protein sequences from the E.cuniculi genome; Fugu rubripes protein sequences from the pufferfish genome; Guillardia theta protein sequences from the G.theta genome; Homo sapiens protein sequences from the human genome; Mus musculus protein sequences from the mouse genome; Neurospora crassa protein sequences from the N.crassa genome; Oryza sativa protein sequences from the rice genome; Plasmodium falciparum protein sequences from the P.falciparum genome; Rattus norvegicus protein sequences from the rat genome; Saccharomyces cerevisiae protein sequences from the yeast genome; Schizosaccharomyces pombe protein sequences from the yeast genome
A database of domain-domain and peptide-mediated interactions of known 3D structures.
The database of 3D Interaction Domains (3did) is a collection of domain-domain and domain-peptide interactions for which high-resolution three-dimensional structures are known. 3did exploits structural information to provide critical molecular details necessary for understanding how these interactions occur. It also offers an overview of how similar in structure are interactions between different members of the same protein family. The database also contains GO-based functional annotations and interactions between yeast proteins from large-scale interaction discovery studies.
Sponsors: This resource is partially supported by the Spanish Ministerio de Educacin y Ciencia (PSE-010000-2007-1 and BIO2007-62426) and the 3D-Repertoire from the European Commission under FP6 contract LSHG-CT-2005-512028.
Keywords: Database, Domain, Peptide, Protein, 3D structure, Interaction, Dimentional, Structure, Molecular, Functional, Annotation, Interaction, Yeast, Study, Research,
A user-friendly and comprehensive software program for multi-dimensional CSI data visualization, spectral processing, localization, quantification and multi-variate analysis.
3D-Interlogs is a database Protein-protein interactions in various evolutionary lineages.
:The 3D-Interologs is a cross-species interacting database inferring from three-dimensional (3D) protein structure complexes and a novel scoring function by using 3D-domain interologs. For a query protein, the 3D-Interologs database utilizes BLAST to identify homologous proteins and the interacting partners from multiple species. Based on the novel scoring function and structure complexes, 3D-Interologs provides the statistic significances, the interacting models (e.g. hydrogen bonds and conserved amino acids), and functional annotations of interacting partners of a query protein. The identification of orthologous proteins of multiple species is able to use to study on protein-protein evolution, protein functions, and cross-referencing of proteins.
:Key words: metabolic and signalling pathways, protein-protein interaction
Resources for macromolecular X-ray crystallography from the Richardson Laboratory, including kinemages (a scientific illustration presented as an interactive computer display), databases, software, training materials and images
A visualization tool based on the VTK library. Its main feature is to measure and display surface-to-surface distance between two triangle meshes using user-specified uniform sampling ( based on the source code of MeshValmet ).
3dMeshMetric also offers all the basic tools to visualize meshes such as color, opacity, smoothing, down sampling or type of representation.
A 3D digital atlas of normal mouse development constructed from magnetic resonance image data. The download is a zipped file containing the six atlases Theiler Stages (ts) 13, 21,23, 24, 25 and 26 and MRI data for an unlabeled ts19 embryo. To view the atlases, download and install MBAT from: http://mbat.loni.ucla.edu
Specimens were prepared in aqueous, isotonic solutions to avoid tissue shrinkage. Limited specimen handling minimized physical perturbation of the embryos to ensure accurate geometric representations of developing mouse anatomy.
Currently, the atlas contains orthogonal sections through MRI volumes, three stages of embryos that have annotated anatomy, photographs of several stages of development, lineage trees for annotated embryos and a gallery of images and movies derived from the annotations. Anatomical annotations can be viewed by selecting a transverse section and selecting a pixel on the displayed slice.
Database of maps showing the sites of modified rRNA nucleotides. Access to the rRNA sequences, secondary structures both with modification sites indicated, 3D modification maps and the supporting tables of equivalent nucleotides for rRNA from model organisms including yeast, arabidopsis, e. coli and human is provided.
This database complements the Yeast snoRNA Database at UMass-Amherst and relies on linking to some content from that database, as well as to others by colleagues in related fields. Therefore, please be very cognizant as to the source when citing information obtained herein. Locations of modified rRNA nucleotides within the 3D structure of the ribosome.
A free, open source software package for visualization and image analysis including registration, segmentation, and quantification of medical image data. Slicer provides a graphical user interface to a powerful set of tools so they can be used by end-user clinicians and researchers alike. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac Os X.
Slicer is based on VTK (http://public.kitware.com/vtk) and has a modular architecture for easy addition of new functionality. It uses an XML-based file format called MRML - Medical Reality Markup Language which can be used as an interchange format among medical imaging applications. Slicer is primarily written in C++ and Tcl.