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Lorién López-Villellas1, Cristian Iñiguez2, Albert Jiménez-Blanco2,3

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Singletrack is a novel algorithm that significantly reduces memory usage in DNA sequence alignment by storing only one dynamic programming matrix. This innovation accelerates genome analyses without compromising alignment accuracy, addressing a key computational bottleneck.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • DNA sequencing advances necessitate improved computational methods for genome data analysis.
  • Classical dynamic programming (DP) algorithms for sequence alignment are memory-intensive, particularly for gap-affine and dual gap-affine alignments.
  • Existing memory-saving strategies often compromise speed or accuracy.

Purpose of the Study:

  • To introduce Singletrack, an efficient algorithm for gap-affine and dual gap-affine sequence alignment backtracing.
  • To reduce the memory footprint of DP-based alignment algorithms while maintaining optimal results.
  • To enhance the performance of existing alignment tools like Suzuki-Kasahara (SK) and Wavefront Alignment (WFA).

Main Methods:

  • Developed Singletrack, a general backtrace method requiring storage of a single DP matrix.
  • Integrated Singletrack with SK and WFA algorithms.
  • Evaluated memory consumption and performance improvements compared to classical DP and other memory-reduction techniques.

Main Results:

  • Singletrack reduces memory consumption for SK by 2×-4× and for WFA by 3×-5×.
  • Accelerates SK implementation by up to 1.4× and WFA by 1.2×-2.1×.
  • Singletrack-accelerated WFA achieves up to 5.2× higher performance than BiWFA, with a practical increase in memory usage.

Conclusions:

  • Singletrack offers a memory-efficient and general solution for sequence alignment backtracing.
  • It significantly enhances the performance of state-of-the-art alignment algorithms.
  • This method addresses the computational bottleneck in genome data analysis.