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Density-reducing Jaccard estimators for sketch-based long read applications.

Tazin Rahman1, Ananth Kalyanaraman2

  • 1School of Electrical Engineering and Computer Science, Washington State University, Pullman, 99164, WA, USA. tazin.rahman@wsu.edu.

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|December 10, 2025
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Summary
This summary is machine-generated.

MHsketch reduces sequence sketch density for long-read mapping, improving speed and memory efficiency without sacrificing accuracy. This method enhances bioinformatics tools by enabling faster and more resource-conscious sequence analysis.

Keywords:
Jaccard estimatorLong read mappingMinhashingSketchingStrobemerSyncmer

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Area of Science:

  • Bioinformatics and Computational Biology
  • Genomics and Sequence Analysis

Background:

  • Sequence sketching is crucial for long-read applications like assembly and mapping, enabling efficient sequence representation.
  • Sketch density, the fraction of sampled k-mers, impacts both memory usage and mapping sensitivity.
  • Reducing sketch density is desirable for memory efficiency but risks decreased accuracy in mapping.

Purpose of the Study:

  • To develop an efficient algorithm for reducing sketch density while maintaining accuracy in long-read mapping.
  • To present MHsketch, a novel method utilizing Jaccard estimators and MinHashing for density reduction.

Main Methods:

  • MHsketch generates a smaller sketch from an initial k-mer set using MinHashing.
  • The algorithm employs Jaccard estimators to guide density reduction in mapping applications.
  • MHsketch is parallelizable, facilitating scalable computation.

Main Results:

  • MHsketch significantly reduces sketch density, leading to substantial performance benefits.
  • Experiments integrating MHsketch with JEM-mapper showed speedups of [Formula: see text] to [Formula: see text] and memory savings of [Formula: see text].
  • MHsketch (using syncmers) achieved high-quality mapping with reduced time-to-solution and memory footprint compared to existing tools.

Conclusions:

  • MHsketch effectively reduces sketch density, offering significant computational advantages for long-read mapping.
  • The method demonstrates a viable approach to balance sketch density reduction with mapping accuracy.
  • MHsketch presents a promising technique for enhancing the efficiency of bioinformatics tools.