CNAseg-a novel framework for identification of copy number changes in cancer from second-generation sequencing data

Motivation: Copy number abnormalities (CNAs) represent an important type of genetic mutation that can lead to abnormal cell growth and proliferation. New high-throughput sequencing technologies promise comprehensive characterization of CNAs. In contrast to microarrays, where probe design follows a carefully developed protocol, reads represent a random sample from a library and may be prone to representation biases due to GC content and other factors. The discrimination between true and false positive CNAs becomes an important issue. Results: We present a novel approach, called CNAseg, to identify CNAs from second-generation sequencing data. It uses depth of coverage to estimate copy number states and flowcell-to-flowcell variability in cancer and normal samples to control the false positive rate. We tested the method using the COLO-829 melanoma cell line sequenced to 40-fold coverage. An extensive simulation scheme was developed to recreate different scenarios of copy number changes and depth of coverage by altering a real dataset with spiked-in CNAs. Comparison to alternative approaches using both real and simulated datasets showed that CNAseg achieves superior precision and improved sensitivity estimates.