Getting More from your NGS Data: CNV Calling of Target Regions
Copy Number Variations (CNVs) play an important role in human health and disease, and the detection of CNVs in clinical samples has the potential to improve clinical diagnoses and inform treatment decisions. Yet until now, if you wanted to have CNVs on your targeted gene samples, you would need an alternative assay such as Chromosomal Microarrays (CMAs).
In this webcast, we will discuss and demonstrate a CNV calling algorithm coming to VarSeq that is:
- Designed specifically for targeted gene panels and exomes
- Builds on and goes beyond best practices of existing NGS calling methods
- Has the precision to detect events ranging from a single-target to whole chromosome
- Takes advantage of the variants in target regions and their allele frequencies
- Designed and being validated in partnership with a clinical lab on clinical samples
- Integrates seamlessly with the VarSeq interpretation workflow and visualization
While we consider the handling of the variety of target panels and exome capture scenarios a process of iterative improvement, we will demonstrate the high precision characteristics of our algorithm on our clinical validation data sets.
About the Presenters
Gabe Rudy is GHI's Vice President of Product Development and team member since 2002. Gabe thrives in the dynamic and fast-changing field of bioinformatics and genetic analysis. Leading a killer team of Computer Scientists and Statisticians in building powerful products and providing world-class support, Gabe puts his passion into enabling Golden Helix's customers to accelerate their research. When not reading or blogging, Gabe enjoys the outdoor Montana lifestyle. But most importantly, Gabe truly loves spending time with his sons and wife. Follow Gabe on Twitter @gabeinformatics.
Nathan Fortier joined the Golden Helix development team in June of 2014 and is a Senior Software Engineer and Field Application Scientist. Nathan obtained his Bachelor’s degree in Software Engineering from Montana Tech University in May 2011 and received a Master’s degree as well as a PhD in Computer Science from Montana State University. Nathan works on data curation, script development, and product code. When not working, Nathan enjoys hiking and playing music.