This month we hosted two, incredible webcasts officially announcing the latest CNV annotation capabilities our Software Engineering Team has been hard at work on for the past couple of months. Our first webcast, Comprehensive Clinical Workflows for Copy Number Variants in VarSeq, was presented by Golden Helix’s VP of Product & Engineering, Gabe Rudy, who reviewed the expanded capabilities of CNV analysis in VarSeq. Steve Hystad, a Field Application Scientist for Golden Helix, followed this up with a fantastic presentation of VS-CNV and what all of these new features really mean and look like for users!
Steve’s webcast generated a lot of really great questions and decided to share these with the community in case anyone else is asking the same thing! We genuinely love answering questions, so if you have your own that isn’t covered here don’t hesitate to send them our way at email@example.com.
Are the CNV annotations a separate add-in that requires an additional license?
No, VarSeq users will not need an additional license to annotate their CNV calls.
If users want to call CNVs from their NGS datasets using our VS-CNV algorithm, then they will need the VS-CNV package. We’d be happy to provide additional details on this VS-CNV algorithm which does include calling Copy Number Variants and Loss-of-Heterozygosity (LOH) variants. So, if this is something you’re interested in talking more about just let us know!
Can I potentially annotate CNVs that were generated by a microarray or other algorithms like Xhmm?
We are currently working on methods to import existing microarray datasets or CNV calls from other algorithms in order for users to take advantage of our powerful annotation pipeline, filtering, and even assessment catalogs tools. If you are interested in this feature, we would be happy to talk to anyone who is willing to share those raw data formats.
Do CNV annotations and reports work with VSWarehouse?
Absolutely! VSWarehouse will be expanded to incorporate new CNV assessment catalogs and reports containing CNVs. With these features, users will be able to store, access, and query any relevant CNV in the data.
How do you choose the reference for CNV analysis?
There are a couple of ‘best practices’ to consider when selecting samples to add to a reference sample set. Control samples with good coverage (~60-100x) over the target regions are best. However, the algorithm will scan the reference sample folder and choose a subset of reference samples to be used as matched controls that best match the sample of interest. We have a great blog post that covers this topic in more detail here.
Will different testing methods for reference versus real samples affect the analysis result?
Samples should be from the same platform and library preparation prior to running the analysis.
When is this new version for CNV going to be released?
Very Soon! Each product released from Golden Helix undergoes extensive testing and scrutiny to ensure all users get the best software solution.
Using well-documented data, how effective is the dbNSFP filter?
The use of any in-silico prediction as a primary reason to classify a variant as pathogenic for a clinical case may not be a best practice. Also, it may be more reasonable to filter out variants where perhaps 5 or 6 out of the 6 functional prediction algorithms predicted a missense variant as benign. Ultimately we feel there can be an improvement on the pre-computed scores that are not transcript aware, and as we mentioned in our last webcast we are looking at making VarSeq do in-silico predictions on the fly based on updated per-transcript gene models. Stay tuned!