Clustering-based optimization method of reference set selection for improved CNV callers performance

Abstract

Background: There are over 25 tools dedicated for the detection of Copy Number Variants (CNVs) using Whole Exome Sequencing (WES) data based on read depth analysis.The tools reported consist of several steps, including: (i) calculation of read depth for each sequencing target, (ii) normalization, (iii) segmentation and (iv) actual CNV calling. The essential aspect of the entire process is the normalization stage, in which systematic errors and biases are removed and the reference sample set is used to increase the signal-to-noise ratio.Although some CNV calling tools use dedicated algorithms to obtain the optimal reference sample set, most of the advanced CNV callers do not include this feature.To our knowledge, this work is the first attempt to assess the impact of reference sample set selection on CNV detection performance. Methods:We used WES data from the 1000 Genomes project to evaluate the impact of various methods of reference sample set selection on CNV calling performance of three chosen state-of-the-art tools: CODEX, CNVkit and exomeCopy. Two naive solutions (all samples as reference set and random selection) as well as two clustering methods (k-means and k nearest neighbours with a variable number of clusters or group sizes) have been evaluated to discover the best performing sample selection method.

ConanVarvar: a versatile tool for the detection of large syndromic copy number variation from whole-genome sequencing data

“…The 1 Mb cutoff on the CNV size allows ConanVarvar to quickly detect tens of known syndromic CNVs without reporting large numbers of false positives. False-positive CNVs are a long-known problem in disease sequencing studies [ 7 , 23 , 24 ], as they tend to complicate the analysis by obscuring the more severe genomic abnormalities.…”