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RabbitKSSD: accelerating genome distance estimation on modern multi-core architectures.

Xiaoming Xu1, Zekun Yin1, Lifeng Yan1

  • 1School of Software, Shandong University, Jinan, China.

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|November 16, 2023
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Summary
This summary is machine-generated.

RabbitKSSD is a new, high-speed tool for genome distance estimation. It significantly speeds up calculations, outperforming existing methods for large-scale genomic analyses.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate and efficient genome distance estimation is crucial for comparative genomics and evolutionary studies.
  • Existing tools often face scalability challenges when analyzing large genomic datasets.

Purpose of the Study:

  • To develop a high-speed tool, RabbitKSSD, for rapid genome distance estimation.
  • To improve the efficiency of sketch generation and distance computation for large-scale genomic data.

Main Methods:

  • Leveraging load-balanced task partitioning for efficient parallel processing.
  • Utilizing fast I/O, high-performance data structures, and optimized intermediate result access.
  • Implementing optimized algorithms for sketch generation and all-vs-all distance computation.

Main Results:

  • RabbitKSSD achieves speedups of 5.7× to 19.8× over Kssd for sketch generation and distance computation.
  • Demonstrates superior performance compared to Mash, BinDash, and Dashing2.
  • Completes all-vs-all distance computation for 455 GB of bacterial genomes in just 2 minutes on a 64-core workstation.

Conclusions:

  • RabbitKSSD offers a significant advancement in high-speed genome distance estimation.
  • The tool provides a scalable and efficient solution for analyzing large genomic datasets.
  • Enables faster comparative genomic analyses, facilitating discoveries in microbial genomics and evolution.