panelcn.MOPS: CNV detection in targeted panel sequencing data for diagnostic use
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.
We have tested panelcn.MOPS on simulated and real sequencing data. The real sequencing data was enriched with the TruSight cancer panel that targets 94 cancer predisposition genes including NF1/2, BRCA1/2 and APC. The performance of panelcn.MOPS was compared on a data set of 150 samples against several CNV detection tools including NextGENe, ExomeDepth, CoNVaDING, and VisCap. The size of the CNVs ranges from a 20bp deletion affecting only part of an exon over duplications of several exons to a 350kb deletion of an entire gene. In contrast to the other methods, panelcn.MOPS not only achieved a sensitivity of 100%, but also the highest specificity. Furthermore, we do not only provide panelcn.MOPS as an R package, but also as a standalone program with a practical graphical user interface. Therefore, panelcn.MOPS can be conveniently used by users without any programming experience.
Our results show that panelcn.MOPS accurately predicts CNVs in targeted panel sequencing data. Consequently complementary biotechnologies to detect CNVs, such as MLPA, can be omitted in order to reduce time and costs.
Povysil G., Tzika A., Vogt J., Haunschmid V., Messiaen L., Wimmer K., Klambauer G., Hochreiter S.,
panelcn.MOPS: CNV detection in targeted panel sequencing data for diagnostic use; (Abstract 1016T).
Presented at the 66th Annual Meeting of The American Society of Human Genetics, October 20, 2016, Vancouver, Canada.
Installer for Windows:
- Please contact email@example.com