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Related Experiment Video

Updated: Dec 10, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

RabbitMash: accelerating hash-based genome analysis on modern multi-core architectures.

Zekun Yin^1,2, Xiaoming Xu¹, Jinxiao Zhang¹

¹School of Software, Shandong University, Jinan 250101, China.

Bioinformatics (Oxford, England)

|August 27, 2020

Summary

This summary is machine-generated.

RabbitMash significantly accelerates genome analysis by optimizing the popular Mash toolkit for modern multi-core processors. This enhanced tool provides substantial speedups for large-scale genomic datasets, improving efficiency in tasks like clustering and assembly.

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Last Updated: Dec 10, 2025

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Mash is a widely used hash-based toolkit for genome analysis.
Current Mash implementations do not fully leverage modern multi-core architectures, leading to long runtimes for large datasets.

Purpose of the Study:

To develop an efficient and highly optimized implementation of Mash.
To enhance the performance of Mash by utilizing modern hardware capabilities.

Main Methods:

Developed RabbitMash, an optimized version of Mash.
Implemented multi-threading, vectorization, and fast I/O to leverage modern hardware.
Compared RabbitMash performance against the original Mash.

Main Results:

RabbitMash achieved speedups of 1.3x (sketch), 9.8x (dist), 8.5x (triangle), and 4.4x (screen) compared to Mash.
Successfully computed all-versus-all distances for 100,321 genomes in under 5 minutes on a 40-core workstation, a task taking Mash over 40 minutes.

Conclusions:

RabbitMash offers significant performance improvements over the standard Mash toolkit.
The optimized implementation enables faster and more efficient large-scale genomic data analysis.