panelcn. MOPS reaches clinical standards as a CNV detection tool for targeted panel sequencing data
Sprache des Titels:
Englisch
Original Buchtitel:
ASHG 2015 Proceedings
Original Kurzfassung:
Targeted panel sequencing is becoming increasingly important as a cost-effective strategy to identify disease-causing variants in clinical and research applications. While various copy number variation (CNV) detection methods exist for whole-genome and whole-exome sequencing data, highly accurate methods for panel sequencing data that are suitable for clinical purposes are still missing. The challenges with this kind of data are 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 even just parts thereof as well as longer CNVs that affect multiple exons or even an entire gene.We present panelcn.MOPS for copy number detection which extends our previously developed method cn.MOPS to targeted panel sequencing data. The method is well suited for this type of data since it can estimate technical and biological characteristics influencing the read counts of each targeted region by a mixture of Poissons model. The design of the count windows, the read counting procedure, the parameters of the model and the segmentation algorithm have been optimized for targeted panel sequencing. cn.MOPS supplies integer copy numbers together with probabilities which inform users about the reliability of the copy number estimates.We have tested panelcn.MOPS on simulated and real sequencing data. On 240 simulated data sets, that resembled the characteristics of targeted panel sequencing data, panelcn.MOPS has reached an average accuracy of 99.96%. The real sequencing data was enriched with the TruSight cancer panel that targets 94 cancer predisposition genes including NF1/2, BRCA1/2 and APC. panelcn.MOPS detected 100% of CNVs known from previous MLPA analyses without any false positives. For whole abstract see http://www.bioinf.jku.at/publications/2015/ASHG2015_Haunschmid.pdf