Enabling efficient and robust analysis of tandem repeats in genomic data using wavefront-based string decomposer

Tandem repeats (TRs) analysis is crucial for understanding genome structure and variation. However, string decomposition, a key challenge in TRs analysis, remains computationally demanding. In this study, we introduce Wavefront-based String Decomposer (WSD), a novel algorithm that enhances efficiency and accuracy in TRs decomposition. By integrating wavefront techniques, WSD significantly reduces computational and memory costs. Additionally, two adaptive strategies minimize parameter sensitivity and further improve efficiency. Through extensive experiments, we demonstrate that WSD outperforms current state-of-the-art (SOTA) methods, achieving an average speedup of ~ 2.33× and reducing memory usage by two orders of magnitude when analyzing human TRs.