Maps clinically significant variants by gene and position!
ClinVar is a freely accessible, public archive of reports of the relationships between human variations and phenotypes, with supporting evidence at NLM/NCBI. To help you access your variants of interest quickly, ClinVar is introducing an all-new visualization tool in the search results. This graphical display provides an overview of variants when you search by gene or genomic region (Figures 1 and 2). You can only get the graphical display with gene or genomic region searches. For other types of searches, you will see the table only.
Gene search display
The display for a gene search highlights small variants within the gene. Large structural variants are also marked as a single dot in the middle of the variation. The interactive display shows the placement of variants on the gene and their clinical significance and allows you to zoom in or pan right / left and limit results to variants in a chosen gene. Figure 1 shows the graphical display as it appears at the top of the search results for the desmoglein 2 (DSG2) gene and how to filter and navigate to variants of interest (Search ClinVar: DSG2[gene]).
B. You can zoom in by mousing over the 8th exon in the gene diagram, which activates a pop-up menu that allows you to re-display only this region by following the link (red box).
C. Refreshed result for the 8th exon of DSG2 showing a number of variants including pathogenic, benign, and ones with conflicting interpretations of pathogenicity. You can select the filters on the left-hand side of the ClinVar result to limit to variants with characteristics of interest, for example Conflicting Interpretations of pathogenicity.
D. Variants in exon 8 of DSG2 filtered for conflicting interpretations of pathogenicity. You can retrieve individual variants by mousing over the graphic to activate the pop-up menu and following the link (red box).
Wondering why 2,100 submitters from 83 countries have deposited more than 1.9 million records of their latest variation information in ClinVar? Curious about why genetic counselors, physicians, researchers, and so many others enthusiastically use data for nearly 1.2 million unique variants in ClinVar? Thinking about becoming part of this global community and sharing your knowledge to further science and make an impact on patient health? Well, we thought we should help you along by making the case for why everyone should submit to ClinVar.
#1: Every deposit can help a patient
The healthcare community relies on the standardized view offered by ClinVar variant reports, which include interpretations of clinical significance in relation to Mendelian disease, cancer and pharmacogenetics; an aggregated view of interpretations highlighting those in consensus, conflict or reviewed by expert panel; and detailed views of submitter data, including supporting evidence for the interpretation such as phenotype, assertion criteria and references.
The Genome Data Viewer (GDV) is now the comprehensive NCBI genome browser. The development of GDV led to a few different types of genome browsers along the way, each one originally delivering visual displays for particular datasets. We developed the 1000 Genomes Browser for variation data from the 1000 Genomes project, the dbGaP Data Browser for controlled-access sequence read alignment data, and the GeT-RM browser for Genome in a Bottle (GIAB) data.
The data displayed in these three browsers is now either obsolete and/or can largely be accessed from the GDV browser or other NCBI resources. Moreover, unlike GDV, these older browsers are no longer under active development and the data has not been updated to meet changing needs of the communities they were developed to serve. For these reasons we will retire these browsers in April 2022. Please see details below for more information on the data displayed in these browsers and how to access and display these data now through GDV and other means.
Attention dbGaP submitters! Join us on November 3, 2021 at 12PM US eastern time to learn about data submission and processing improvements to dbGaP, NIH’s database of Genotype and Phenotype, which contains individual-level data associated with human research studies. You will see how we have made submission easier through the Submission Portal using automated preliminary validation and how you can use GaPTools, a stand-alone data validation tool, on your own submission to expedite the submission process. Join us to discover how dbGaP ensures integrity and high-quality in the genomic data that scientists can access to further their research.
NIH’s data sharing policy now allows unrestricted access to genomic summary results for data from NCBI’s Database of Genotypes and Phenotypes (dbGaP). Pooled allele frequency data from dbSNP and the dbGaP summary results are available as the new Allele Frequency Aggregator (ALFA) dataset. The ALFA dataset includes aggregated and harmonized array chip genotyping, exome, and genome sequencing data. The ALFA data are open access and freely available for you to incorporate into your workflows and applications from the dbSNP web pages (Figure 1), through FTP,and the Variation Services API. dbGaP currently has data for more than 2 million study subjects, approximately 1 million of whom have genotype data that is suitable for input into the ALFA dataset. The first release of ALFA contains data on about 100,000 subjects, and we hope to complete processing of data on the other 925,000 subjects within the next year. This volume and variety of data promises unprecedented opportunities to identify genetic factors that influence health and disease. Register to attend our April 22 webinar and read on to learn more.
Figure 1. ALFA allele frequencies for a variant (rs4988235) in the promotor of the lactase gene showing frequency differences across populations.
Check out the latest videos on YouTube to learn how to best use NCBI graphical viewers, SRA, PGAP, and other resources.
Genome Data Viewer: Analyzing Remote BAM Alignment Files and Other Tips
This video shows you how to upload remote BAM files, and succinctly demonstrates handy viewer settings, such as Pileup display options, and highlights the very helpful tooltips in the Genome Data Viewer (GDV). There’s also a brief blog post on the same topic.