dbVar, NCBI’s database of large-scale genetic variants, has a new track hub for viewing and downloading structural variation (SV) data in popular genome browsers. Initial tracks include Clinical and Common SV datasets. dbVar’s new track hub can be viewed using NCBI’s Genome Data Viewer through the “User Data and Track Hubs” feature (Figure 1) and other genome browsers by selecting “dbVar Hub” from the list of public tracks or by specifying the following URL.
The latest dbVar data release includes the Genome in a Bottle benchmark structural variant (SV) callset (pre-print Zook et al. 2019) – a highly scrutinized, carefully curated set of 12,745 sequence-resolved deletions, insertions, and delins variants from Personal Genome Project Ashkenazi trio son HG002. The data serve as a robust benchmark standard with which to measure the performance of sequencing and variant-calling pipelines. It “reliably identifies both false negatives and false positives in high-quality SV callsets” (pre-print Zook et al. 2019) that are based on short-, linked-, and long-read sequencing as well as optical mapping.
We’ve expanded the catalog of clinically relevant structural variants (SV) in dbVar by adding 57,520 ClinVar records. You can access the newly added data through study nstd102.
The updated collection includes:
20,000 new SVs, and more than 37,000 copy number variants (CNV) observed in ClinGen laboratories during routine cytogenomic laboratory testing that were previously accessioned separately at dbVar
15,000 SVs asserted as ‘Pathogenic’ or ‘Likely pathogenic’ for thousands of clinical genetic disorders including breast, ovarian, and colon cancers; hypercholesterolemia; schizophrenia; Duchenne Muscular Dystrophy; autism spectrum disorders; and many others
You can browse dbVar studies on the web or download the data. We provide dbVar data in a number of standard formats (VCF, GVF, and TSV) mapped to assemblies GRCh38, GRCh37, and NCBI36 allowing you perform analysis using standard tools and integrate the data into your bioinformatic workflows.
Visit our Walkthrough pageto learn how to use these new dbVar data to help interpret structural variation in your favorite gene or genomic region.
Would you like to compare and analyze your data with known structural variants (SV) in NCBI’s database of genomic structural variation (dbVar)? Now there are easy-to-use files containing non-redundant (NR) deletions, duplications, and insertions aggregated from across studies in dbVar. The files are available for human assembly versions GRCh37 and GRCh38. Descriptions of the NR data are available on GitHub.
The NR files are available for FTP download in BED, BEDPE, and custom tab-separated formats, designed to be compatible with many popular tools and browsers. To help users get started, we have developed tutorials for UCSC Genomic Browser, Galaxy web-based analysis platform, NCBI Sequence Viewer, and command-line BEDtools.
An upcoming release will include annotations including genes, regulatory regions, and more. Have a favorite annotation you’d like to see? Send us your suggestions by contacting dbVar directly or open a GitHub issue. We also welcome comments and other improvement suggestions.
dbVar non-redundant SV (NR SV) datasets include more than 2.2 million deletions, 1.1 million insertions, and 300,000 duplications. These data are aggregated from over 150 studies including 1000 Genomes Phase 3, Simons Genome Diversity Project, ClinGen, ExAC, and others. You can use NR SV data files to filter and annotate variants in a broad range of applications:
Clinicians can easily filter patients’ genome data to find SV that overlap with variants previously reported as clinically significant.
Researchers can compare the results of their own genome-wide SV surveys with dbVar NR data to identify variants that are novel or rare, those which may be pathogenic, and in some cases obtain allele frequencies for matching variants. Users can also annotate SV data with NR SV and other genomic annotations to prioritize those variants most likely to impact biological function.
Developers of variant analysis pipelines can use dbVar NR data to help identify novel variants, calibrate their algorithms, or simply integrate the data into downstream analysis tools and workflows.
dbVar’s NR SV reference data are updated monthly. These updates include new database submissions. We welcome your feedback on the content and usability of these files so that we can improve them.
For more information, please see our GitHub site, which includes brief tutorials and access to NR SV datasets by >FTP.
The new services are faster, better at handling variants in repeat regions, and scalable to accommodate the continued explosive growth of variation volume. You can find more information about the services in the initial blog post and online SPDI document.
If you would like to report any issues related to these new services and/or would like to provide comments, please write to firstname.lastname@example.org.
dbVar has generated known structural variants (SV) datasets for use in comparisons with user data to aid variant calling, analysis and interpretation.
Files containing Non-Redundant (NR) deletions, insertions, and duplications are now available on GitHub. Additional separate files include preliminary annotations of overlap with ACMG59 genes. All files are in tab-delimited text format.
We encourage you to test these files and provide feedback, either on GitHub or by email.
RefSeq release 85 is now accessible online, via FTP and through NCBI’s programming utilities. This full release incorporates genomic, transcript, and protein data available, as of November 6, 2017, and contains 146,710,309 records, including 100,043,962 proteins, 20,905,608 RNAs, and sequences from 73,996 organisms. The release is provided in several directories as a complete dataset and as divided by logical groupings. See the RefSeq release notes for more information.
Starting in March 2018, SNP variation features will no longer be in RefSeq genome assembly records – chromosome and contig records with NC_, NT_, NW_ and AC_ accession prefixes. This change affects both the ASN.1 and flatfile records. Because the number of variants is already enormous and still growing, removing SNP features from these large genomic records will significantly reduce the size of RefSeq FTP files and make downloading and processing easier. We will continue to include SNPs on NG_-prefixed genomic records, and transcript (NM_, NR_, XM_, XR_) and protein (NP_, XP_, YP_) sequences.