Bias and Noise Cancellation for Robust Copy Number Variation Detection

High-throughput next generation sequencing (NGS) technologies have created an opportunity for detecting copy number variations (CNVs) more accurately. In this work, we introduce a novel preprocessing pipeline to improve the detection accuracy of CNVs in heterogeneous NGS data such as cancer whole exome sequencing data. We employ several normalizations to reduce biases due to GC contents, mappability and tumor contamination. We also utilize the Taut String method as an efficient effective smoothing approach to reduce noise.