Enabling efficient and robust analysis of tandem repeats in genomic data using Wavefront-based String Decomposer.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Tandem repeat (TR) analysis is vital for understanding genome structure and variation.
• String decomposition for TR analysis presents significant computational challenges.
• Existing methods often require substantial computational and memory resources.

Purpose of the Study:

• Introduce Wavefront-based String Decomposer (WSD), a novel algorithm for efficient and accurate TR decomposition.
• Improve the computational efficiency and reduce memory footprint in TR analysis.
• Address the limitations of current state-of-the-art (SOTA) methods.

Main Methods:

• Developed a novel algorithm integrating wavefront techniques for string decomposition.
• Implemented two adaptive strategies to minimize parameter sensitivity and enhance efficiency.
• Evaluated WSD performance against SOTA methods using extensive experimental datasets.

Main Results:

• WSD demonstrates significant reductions in computational and memory costs.
• Achieved an average speedup of approximately 2.33× compared to SOTA methods.
• Reduced memory usage by two orders of magnitude in human TR analysis.

Conclusions:

• WSD offers a substantial improvement in efficiency and accuracy for TR decomposition.
• The algorithm effectively addresses the computational demands of analyzing complex genomic structures.
• WSD represents a significant advancement for genomic research involving tandem repeat analysis.