Searching across hundreds of databases
Accurate interpretation of DNA sequence variation is a prerequisite for implementing personalized medicine. Discrepancies in interpretation between testing laboratories impede the effective use of genetic test results in clinical medicine. To better understand the underpinnings of these discrepancies, we quantified differences in variant classification internally over time and those between our diagnostic laboratory and other laboratories and resources. We assessed the factors that contribute to these discrepancies and those that facilitate their resolution. Our process resolved 72% of nearly 300 discrepancies between pairs of laboratories to within a one-step classification difference and identified key sources of data that facilitate changes in variant interpretation. The identification and harmonization of variant discrepancies will maximize the clinical use of genetic information; these processes will be fostered by the accumulation of additional population data as well as the sharing of data between diagnostic laboratories.
Pubmed ID: 27843123 RIS Download
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Database as central repository for both single base nucleotide substitutions and short deletion and insertion polymorphisms. Distinguishes report of how to assay SNP from use of that SNP with individuals and populations. This separation simplifies some issues of data representation. However, these initial reports describing how to assay SNP will often be accompanied by SNP experiments measuring allele occurrence in individuals and populations. Community can contribute to this resource.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. An aggregated data platform for genome sequencing data created by a coalition of investigators seeking to aggregate and harmonize exome sequencing data from a variety of large-scale sequencing projects, and to make summary data available for the wider scientific community. The data set provided on this website spans 61,486 unrelated individuals sequenced as part of various disease-specific and population genetic studies. They have removed individuals affected by severe pediatric disease, so this data set should serve as a useful reference set of allele frequencies for severe disease studies. All of the raw data from these projects have been reprocessed through the same pipeline, and jointly variant-called to increase consistency across projects. They ask that you not publish global (genome-wide) analyses of these data until after the ExAC flagship paper has been published, estimated to be in early 2015. If you''re uncertain which category your analyses fall into, please email them. The aggregation and release of summary data from the exomes collected by the Exome Aggregation Consortium has been approved by the Partners IRB (protocol 2013P001477, Genomic approaches to gene discovery in rare neuromuscular diseases).
View all literature mentionsArchive of aggregated information about sequence variation and its relationship to human health. Provides reports of relationships among human variations and phenotypes along with supporting evidence. Submissions from clinical testing labs, research labs, locus-specific databases, expert panels and professional societies are welcome. Collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about submitter, and other supporting data. Alleles described in submissions are mapped to reference sequences, and reported according to HGVS standard.
View all literature mentionsOnline catalog of human genes and genetic disorders, for clinical features, phenotypes and genes. Collection of human genes and genetic phenotypes, focusing on relationship between phenotype and genotype. Referenced overviews in OMIM contain information on all known mendelian disorders and variety of related genes. It is updated daily, and entries contain copious links to other genetics resources.
View all literature mentionsInternational 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
View all literature mentionsA 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.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
scicrunch.org
