QuickSeg: A fast, versatile and accurate algorithm for genomic copy number segmentation using dynamic programming

Abstract

Copy number alterations are among the most common genomic aberrations in cancer and their accurate identification relies on robust segmentation of sequencing read-depth signals. Existing segmentation methods typically balance computational efficiency against segmentation accuracy and remain sensitive to technical artifacts present in sequencing data. Here, we present QuickSeg, a fast and versatile methodology that uses an exact dynamic programming algorithm to detect copy number segments using median-based error function. Motivated by the observation that sequencing depth distributions contain a small but pervasive population of outlying observations, this approach provides increased robustness to technical noise while simultaneously reducing the computational complexity of the segmentation problem. Across whole-genome sequencing of cancer cohorts, using breakpoint-supported somatic copy number alterations, we demonstrate improved segmentation precision over two widely used baseline methods, Circular Binary Segmentation (CBS) and Piecewise Constant Fitting (PCF), across a broad range of sensitivity thresholds. QuickSeg also consistently outperformed both methods with respect to runtime and memory usage. Collectively, our results show that robust median-based optimization provides both biological and computational advantages for copy number segmentation, enabling accurate analysis of large sequencing cohorts with minimal computational requirements.