Polishing Copy Number Variant Calls on Exome Sequencing Data via Deep Learning

Accurate and efficient detection of copy number variants (CNVs) is of critical importance due to their significant association with complex genetic diseases. Although algorithms working on whole genome sequencing (WGS) data provide stable results with mostly-valid statistical assumptions, copy number detection on whole exome sequencing (WES) data has mostly been a losing game with extremely high false discovery rates. This is unfortunate as WES data is cost efficient, compact and is relatively ubiquitous. The bottleneck is primarily due to non-contiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data and learns to correct the copy number variations reported by any over-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that (i) we can efficiently triple the duplication call precision and double the deletion call precisions of the state-of-the-art algorithms. We also show that model consistently improves the performance in a (i) sequencing technology, (ii) exome capture kit and (iii) CNV caller independent manner. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets and surge its application.