Methods for DNA Copy Number Variation Analysis Using High-Throughput Sequencing

Abstract

Copy Number Variation (CNV) is an important class of genetic variation, which has been traditionally studied using microarray-based Comparative Genomic Hybridization. Recently the next-generation sequencing technologies have revolutionized biological research.

We developed one of the first methods to detect CNV utilizing DNA sequencing, which we call CNV-seq. This method is based on a robust statistical model that describes the complete analysis procedure and allows the computation of essential confidence values for detection of CNV. The statistical model also shows that the next-generation sequencing technologies are more suitable for CNV-seq than traditional sequencing technologies.

Based on the statistical model of CNV-seq, we also developed a two-stage Hidden Markov Model, CNV-segHMM for analyzing CNV-seq data. The resolution of CNV boundary detection by the HMM approach is the distance between two adjacent mapped sequencing reads, which is the highest possible resolution. By increasing the number of reads sequenced, single-nucleotide resolution can be achieved. Together with the increasing speed and decreasing cost of sequencing technologies, we expect our CNV-seq framework and the CNV-segHMM tool to be widely used.