a0f7abd772e0e0b47c4a64ffdc43527b39454ccf gperez2 Thu Dec 11 21:27:58 2025 -0800 Updating the data of the gnomAD v4.1 CNV track with the gnomad.v4.1.cnv.all.bed data, refs #36788 diff --git src/hg/makeDb/trackDb/human/hg38/gnomadCNV.html src/hg/makeDb/trackDb/human/hg38/gnomadCNV.html index 8738522aff9..44a994b5efc 100644 --- src/hg/makeDb/trackDb/human/hg38/gnomadCNV.html +++ src/hg/makeDb/trackDb/human/hg38/gnomadCNV.html @@ -2,35 +2,35 @@
The ${longLabel} track set shows rare autosomal coding copy number variants (CNVs) with an overall site frequency of less than 1%. These variants were identified from exome sequencing (ES) data of 464,297 individuals. The data can also be explored via the gnomAD browser.
Items are colored by the type of variant:
| Variant Type | ||
|---|---|---|
| Deletion (DEL) | -20989 | +31939 |
| Duplication (DUP) | -25026 | +36760 |
Mouseover on an item will display the position, size of variant, genes impacted by variant (>=10% CDS overlap by deletion or >=75% CDS overlap by duplication), and site frequency of non-neuro control samples. Item description pages include a linkout to the gnomAD browser showing additional genetic ancestry group information.
To identify rare coding CNVs from the ES data of 464,297 individuals in gnomAD v4, the GATK-gCNV method was employed, as described in Babadi et al., Nat Genet, 2023.
@@ -65,31 +65,31 @@ dataset.More information can be found at the gnomAD site.
The bed files was obtained from the gnomAD Google Storage bucket:
-https://storage.googleapis.com/gcp-public-data--gnomad/release/4.1/exome_cnv/gnomad.v4.1.cnv.non_neuro_controls.bed +https://storage.googleapis.com/gcp-public-data--gnomad/release/4.1/exome_cnv/gnomad.v4.1.cnv.all.bedThe data was then transformed into a bigBed track. For the full list of commands used to make this track please see the "gnomAD CNVs v4.1" section of the makedoc.
The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated access, this track, like all others, is available via our API. However, for bulk processing, it is recommended to download the dataset. The genome annotation is stored in a bigBed file that can be downloaded from the