View GEO, SRA, or dbGaP data tracks in NCBI’s Genome Data Viewer

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).

screenshot of genome data browser, showing 'Tracks' menu and 'Find Tracks' tab
Figure 1. Go to the ‘Tracks’ menu on the browser toolbar and select ‘Configure Tracks’ option. This will launch a panel where you can add, configure, remove, and search for data tracks. Go to the ‘Find Tracks’ tab to search for tracks to add to your browser view. Note: spaces act as AND operators in the search, and wildcards are accepted.

You can search for compatible tracks from the GEO, SRA, or dbGaP databases (Figure 2).

An example of a search within the GEO database
Figure 2. An example of a search within the GEO database, using two search terms and a wildcard *, showing two results. Click on the checkboxes to add the tracks you want, then select the Configure button at the bottom to close the panel and view the tracks aligned to the genome assembly, as shown below in Figure 3.

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.

Genome browser view at the human JUN gene showing NCBI gene annotation, Ensembl gene annotation, Cited Variations from dbSNP, as well as two H3K4me3 experimental data tracks from the GEO database (the first two arrows, in red) and an RNA-seq study from the SRA database (the last arrow, in blue).
Figure 3. Genome browser view at the human JUN gene showing NCBI gene annotation, Ensembl gene annotation, Cited Variations from dbSNP, as well as two H3K4me3 experimental data tracks from the GEO database (the first two arrows, in red) and an RNA-seq study from the SRA database (the last arrow, in blue). Original: https://go.usa.gov/xMGBx

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.

Genome browser view showing NCBI gene annotation along with a GWAS study track from dbGaP (red arrow).
Figure 4. Genome browser view showing NCBI gene annotation along with a GWAS study track from dbGaP (red arrow). The color and height of the dots in the scatterplot correspond to the negative log P-value of the association of a SNP at that position with the study trait. Original: https://go.usa.gov/xMGBy

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.

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