panelcn.MOPS: CNV detection in targeted panel sequencing data for diagnostic use
Sprache des Titels:
ASHG 2016 Proceedings
While various copy number variation (CNV) detection methods exist for whole-genome and whole-exome sequencing data, highly accurate methods for targeted panel sequencing data that are suitable for a diagnostic setting are still missing. The challenges with analyzing this kind of data include the small size and number of target regions as well as their uneven coverage. For clinical applications a method should furthermore be able to detect both short CNVs affecting only single exons or just parts thereof as well as longer CNVs that affect multiple exons or even an entire gene. Another important issue is the risk of incidental findings.
Our new method panelcn.MOPS for copy number detection extends cn.MOPS to targeted panel sequencing data. We optimized the design of the count windows, the read counting procedure, the parameters of the model and the segmentation algorithm for targeted panel sequencing. Additionally, several quality control criteria both for samples and targeted exons have been implemented to increase the confidence in called CNVs. In contrast to other CNV detection methods all targeted regions are exploited for the detection of CNVs, but only results for user-selected genes are reported to avoid the risk of incidental findings.