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Found 15 result(s)
Born of the desire to systematize analyses from The Cancer Genome Atlas pilot and scale their execution to the dozens of remaining diseases to be studied, GDAC Firehose now sits atop terabytes of analysis-ready TCGA data and reliably executes thousands of pipelines per month. More information: https://broadinstitute.atlassian.net/wiki/spaces/GDAC/
The ENCODE Encyclopedia organizes the most salient analysis products into annotations, and provides tools to search and visualize them. The Encyclopedia has two levels of annotations: Integrative-level annotations integrate multiple types of experimental data and ground level annotations. Ground-level annotations are derived directly from the experimental data, typically produced by uniform processing pipelines.
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The Human Genetic Variation Database (HGVD) aims to provide a central resource to archive and display Japanese genetic variation and association between the variation and transcription level of genes. The database currently contains genetic variations determined by exome sequencing of 1,208 individuals and genotyping data of common variations obtained from a cohort of 3,248 individuals.
<<<!!!<<< NCBI announced plans to retire the Clone DB web interface. Pursuant to this retirement, starting on May 27, 2019, all web pages associated with Clone DB and CloneFinder will redirect to this blog post https://ncbiinsights.ncbi.nlm.nih.gov/?s=clone+db. Links to Clone DB from the NCBI home page will also be going away. >>>!!!>>>
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We developed a method, ChIP-sequencing (ChIP-seq), combining chromatin immunoprecipitation (ChIP) and massively parallel sequencing to identify mammalian DNA sequences bound by transcription factors in vivo. We used ChIP-seq to map STAT1 targets in interferon-gamma (IFN-gamma)-stimulated and unstimulated human HeLa S3 cells, and compared the method's performance to ChIP-PCR and to ChIP-chip for four chromosomes.For both Chromatin- immunoprecipation Transcription Factors and Histone modifications. Sequence files and the associated probability files are also provided.
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SILVA is a comprehensive, quality-controlled web resource for up-to-date aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains alongside supplementary online services. In addition to data products, SILVA provides various online tools such as alignment and classification, phylogenetic tree calculation and viewer, probe/primer matching, and an amplicon analysis pipeline. With every full release a curated guide tree is provided that contains the latest taxonomy and nomenclature based on multiple references. SILVA is an ELIXIR Core Data Resource.
>>>>!!!!<<<< The Cancer Genomics Hub mission is now completed. The Cancer Genomics Hub was established in August 2011 to provide a repository to The Cancer Genome Atlas, the childhood cancer initiative Therapeutically Applicable Research to Generate Effective Treatments and the Cancer Genome Characterization Initiative. CGHub rapidly grew to be the largest database of cancer genomes in the world, storing more than 2.5 petabytes of data and serving downloads of nearly 3 petabytes per month. As the central repository for the foundational genome files, CGHub streamlined team science efforts as data became as easy to obtain as downloading from a hard drive. The convenient access to Big Data, and the collaborations that CGHub made possible, are now essential to cancer research. That work continues at the NCI's Genomic Data Commons. All files previously stored at CGHub can be found there. The Website for the Genomic Data Commons is here: https://gdc.nci.nih.gov/ >>>>!!!!<<<< The Cancer Genomics Hub (CGHub) is a secure repository for storing, cataloging, and accessing cancer genome sequences, alignments, and mutation information from the Cancer Genome Atlas (TCGA) consortium and related projects. Access to CGHub Data: All researchers using CGHub must meet the access and use criteria established by the National Institutes of Health (NIH) to ensure the privacy, security, and integrity of participant data. CGHub also hosts some publicly available data, in particular data from the Cancer Cell Line Encyclopedia. All metadata is publicly available and the catalog of metadata and associated BAMs can be explored using the CGHub Data Browser.
<<<!!!<<< OFFLINE >>>!!!>>> A recent computer security audit has revealed security flaws in the legacy HapMap site that require NCBI to take it down immediately. We regret the inconvenience, but we are required to do this. That said, NCBI was planning to decommission this site in the near future anyway (although not quite so suddenly), as the 1,000 genomes (1KG) project has established itself as a research standard for population genetics and genomics. NCBI has observed a decline in usage of the HapMap dataset and website with its available resources over the past five years and it has come to the end of its useful life. The International HapMap Project is a multi-country effort to identify and catalog genetic similarities and differences in human beings. Using the information in the HapMap, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. The Project is a collaboration among scientists and funding agencies from Japan, the United Kingdom, Canada, China, Nigeria, and the United States. All of the information generated by the Project will be released into the public domain. The goal of the International HapMap Project is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. By making this information freely available, the Project will help biomedical researchers find genes involved in disease and responses to therapeutic drugs. In the initial phase of the Project, genetic data are being gathered from four populations with African, Asian, and European ancestry. Ongoing interactions with members of these populations are addressing potential ethical issues and providing valuable experience in conducting research with identified populations. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. The Project officially started with a meeting in October 2002 (https://www.genome.gov/10005336/) and is expected to take about three years.
The Cancer Immunome Database (TCIA) provides results of comprehensive immunogenomic analyses of next generation sequencing data (NGS) data for 20 solid cancers from The Cancer Genome Atlas (TCGA) and other datasource. The Cancer Immunome Atlas (TCIA) was developed and is maintained at the Division of Bioinformatics (ICBI). The database can be queried for the gene expression of specific immune-related gene sets, cellular composition of immune infiltrates (characterized using gene set enrichment analyses and deconvolution), neoantigens and cancer-germline antigens, HLA types, and tumor heterogeneity (estimated from cancer cell fractions). Moreover it provides survival analyses for different types immunological parameters. TCIA will be constantly updated with new data and results.
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The Small Molecule Pathway Database (SMPDB) contains small molecule pathways found in humans, which are presented visually. All SMPDB pathways include information on the relevant organs, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Accompanying data includes detailed descriptions and references, providing an overview of the pathway, condition or processes depicted in each diagram.
The Conserved Domain Database is a resource for the annotation of functional units in proteins. Its collection of domain models includes a set curated by NCBI, which utilizes 3D structure to provide insights into sequence/structure/function relationships
The Human Ageing Genomic Resources (HAGR) is a collection of databases and tools designed to help researchers study the genetics of human ageing using modern approaches such as functional genomics, network analyses, systems biology and evolutionary analyses.
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KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies