Displaying results 1 - 20 out of 13953 total results.
A Biomedical Text Mining platform that copes with major Information Retrieval and Information Extraction tasks and promotes multi-disciplinary research. It aims to provide support to three different usage roles: biologists, text miners and application developers. The workbench supports the retrieval, processing and annotation of documents as well as their analysis at different levels.
The project will focus on patients with a rare disease and their families and patients with cancer. The first samples for sequencing are being taken from patients living in England with discussions taking place with Scotland, Wales and Northern Ireland about potential future involvement. Genomics England, a company wholly owned and funded by the Department of Health, was set up to deliver this flagship project which will sequence 100,000 whole genomes from NHS patients by 2017. Its four main aims are; to create an ethical and transparent programme based on consent; to bring benefit to patients and set up a genomic medicine service for the NHS; to enable new scientific discovery and medical insights; and to kick start the development of a UK genomics industry.
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. 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. Objectives: # 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.
International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. 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. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes
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 registry which provides links and reviews for material resources such as reagents, equipment, digital tools, and providers, as well as the companies that sell them. Users can search for specific materials or search by vendor, product, or service type.
This project is meant for planning the NITRC Grantee meetings. The meetings introduce NITRC participants to one another, promote discussion of common interests, and identify opportunities for collaboration and interoperability. The 2009 meeting for NITRC enhancement grant awardees was held on June 18, 2009 (8:00 AM - 5:00 PM) in San Francisco at the San Francisco Marriott.
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.
Database of physical protein-protein interactions across multiple genomes. Based on 3D-domain interolog mapping and a scoring function, protein-protein interactions are inferred by using three-dimensional (3D) structure heterodimers to search the UniProt database. For a query protein, the database utilizes BLAST to identify homologous proteins and the interacting partners from multiple species. Based on the scoring function and structure complexes, it 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 allows the study of protein-protein evolution, protein functions, and cross-referencing of proteins.
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.