LOVD Annotations in VarSeq for Variants and CNVs

· Nathan Fortier · Public Data & Annotations
Leiden Open Variation Database (LOVD) Logo

The Leiden Open Variation Database (LOVD) is an online database designed to facilitate the collection and display of clinically classified variants. Since its initial release in 2004, LOVD has evolved significantly, with the latest version, LOVD 3.0, greatly expanding the number of annotated variants.

Golden Helix is excited to announce the release of two new annotation tracks containing data from LOVD 3.0:

  1. LOVD Variants: This track contains small variants, providing clinical classifications for SNPs, insertions, and small deletions.
  2. LOVD CNVs and Large Variants: This track contains various large variants, including deletions, duplications, and inversions.

Both annotations contain LOVD classifications and can be effectively utilized alongside our ClinVar annotations to filter out benign variants and identify potentially pathogenic mutations. While LOVD is not as extensive as ClinVar, it offers unique value by providing clinical classifications for variants that may not be present in ClinVar. In fact, 41% of LOVD variants are absent from ClinVar, highlighting the importance of using multiple databases to obtain a more comprehensive understanding of a variant’s clinical significance.

The new LOVD annotation tracks offer a helpful resource for the clinical classification of variants and CNVs. Integrating LOVD with our existing ClinVar annotations provides our users with a powerful combined resource for the identification of pathogenic variants.

If you want to add this feature to your VarSeq license and incorporate this powerful annotation into your variant interpretation workflow, don’t hesitate to get in touch with our team at [email protected].

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Nathan Fortier

About Nathan Fortier

Nathan Fortier, Ph.D, Director of Research for Golden Helix, joined the development team in June of 2014. Nathan obtained his Bachelor’s degree in Software Engineering from Montana Tech University in May 2011, received a Master’s degree in Computer Science from Montana State University in May 2014, and received his Ph.D. in Computer Science from Montana State University in May 2015. Nathan works on data curation, script development, and product code. When not working, Nathan enjoys hiking and playing music.

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