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

被引：1

作者：

Xu, Xiaoming ^{[1
]}

Yin, Zekun ^{[1
]}

Yan, Lifeng ^{[1
]}

Yi, Huiguang ^{[2
]}

Wang, Hua ^{[1
]}

Schmidt, Bertil ^{[3
]}

Liu, Weiguo ^{[1
]}

机构：

[1] Shandong Univ, Sch Software, Jinan, Peoples R China

[2] Chinese Acad Agr Sci, Agr Genom Inst, Genome Anal Lab,Minist Agr, Guangdong Lab Lingnan Modern Agr,Shenzhen Branch, Shenzhen, Peoples R China

[3] Johannes Gutenberg Univ Mainz, Inst Comp Sci, Mainz, Germany

来源：

BIOINFORMATICS | 2023年 / 39卷 / 11期

关键词：

D O I：

10.1093/bioinformatics/btad695

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We propose RabbitKSSD, a high-speed genome distance estimation tool. Specifically, we leverage load-balanced task partitioning, fast I/O, efficient intermediate result accesses, and high-performance data structures to improve overall efficiency. Our performance evaluation demonstrates that RabbitKSSD achieves speedups ranging from 5.7 x to 19.8 x over Kssd for the time-consuming sketch generation and distance computation on commonly used workstations. In addition, it significantly outperforms Mash, BinDash, and Dashing2. Moreover, RabbitKSSD can efficiently perform all-vs-all distance computation for all RefSeq complete bacterial genomes (455 GB in FASTA format) in just 2 min on a 64-core workstation.

引用

页数：4

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