Did you know that you can see epigenomic or other experimental data in NCBI’s Genome Data Viewer (GDV)?
You can easily add aligned study results from GEO, SRA, and dbGaP as data tracks to GDV browser view. Just go to the Tracks button on the toolbar and select the menu option to Configure Tracks. Navigate to the ‘Find Tracks’ tab on the pop-up Configure panel (Figure 1).
You can search for compatible tracks from the GEO, SRA, or dbGaP databases (Figure 2).
If you don’t see any tracks when you select a database, there is no aligned data available to load for the assembly you are viewing. The default search filter ‘Available Tracks’ searches all tracks that were pre-loaded by the graphical viewer team into your browser session, which may include gene annotation, assembly components, SNP, RNA-Seq, and other types of data.
When looking at GDV, you’ll notice that the tracks from GEO are graph-style, while sequence data from SRA are shown as sequence alignments. Data tracks from dbGaP display significantly-associated SNPs as a scatterplot, with the color and Y-axis position of SNPs corresponding to the P-value in the selected GWAS study (Figure 4). Refer to the complete Legends for the NCBI graphical viewers for more information on the visual rendering of the data in these tracks and how to configure your graphical display even more.
As always, please contact us if you have questions or suggestions about this or any other feature in GDV. You can use the Feedback button on the page or write to the NCBI Help Desk directly.
The GEO, SRA, and dbGaP database repositories at NCBI store thousands of experimental studies from research groups across the world. Data stored in GEO includes epigenomic experiments (e.g., ChIP-seq) as well as other molecular genomic data. SRA indexes RNA or DNA sequence alignments from high throughput sequencing studies. dbGaP supplies analyses of human GWAS (genome-wide association study) projects, including reporting the genomic locations of significant P-value SNPs. Experimental data from these NCBI databases can help you validate gene annotation, explore the regulation of different genes or gene splice forms, and create hypotheses for function or disease relevance of your study region.