This webcast generated some great questions! If you have any other questions for me that are not answered below, please feel free to ask those by emailing firstname.lastname@example.org.
To what level does the Warehouse scale?
We have tested multiple instances of Warehouse in-house and on the cloud and it scales incredibly well to tens of thousands of samples and 100s of millions of variants. This has been a primary focus of warehouse since the beginning, and we work hard to ensure that customers will be able to rely on the warehouse to be the storage platform for all their future planning and expected to sequence of gene panels, exomes or genomes.
How do I embed the Warehouse into our existing hospital infrastructure?
The warehouse has an incredibly versatile/powerful API and allows it to be integrated into some other systems. Our users routinely utilize Warehouse with their existing LIMS and EMR systems.
Can we import external CNV calls generated by microarrays or other bioinformatics pipelines?
YES! We are adding this often-requested capability into the upcoming version of VarSeq. You will be able to import any CNV files and leverage the CNV annotation, filtering, assessment catalog saving and reporting capabilities we demonstrated today regardless of how your CNVs were called.
How do you eliminate artifacts in your CNV calling?
There are some methods to help isolate true positive CNV events with VarSeq. The focus of this webcast was to utilize cohort data stored in VSWarehouse to remove commonly seen CNVs that are likely artifacts. However, even without the implementation of VSWarehouse, users have access to many quality fields that can be utilized in their CNV workflow to help isolate true events.
This includes the “Flag” field which lists quality flags for your events, and also the “P-value” field to help define the confidence in the calls. You may also consider the quality of the sample being investigated to ensure there are no sample quality flags before deep diving into events. Here are some resources for CNV analysis that will explain these steps in more detail.