利用位置布隆-惠勒变换（PBWT）算法中的并行化实现高效单倍型匹配与压缩。
Exploiting parallelization in positional Burrows-Wheeler transform (PBWT) algorithms for efficient haplotype matching and compression.
作者信息
Wertenbroek Rick, Xenarios Ioannis, Thoma Yann, Delaneau Olivier
机构信息
School of Engineering and Management Vaud (HEIG-VD), HES-SO University of Applied Sciences and Arts Western Switzerland, Yverdon-les-Bains 1401, Switzerland.
Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.
出版信息
Bioinform Adv. 2023 Mar 2;3(1):vbad021. doi: 10.1093/bioadv/vbad021. eCollection 2023.
SUMMARY
The positional Burrows-Wheeler transform (PBWT) data structure allows for efficient haplotype data matching and compression. Its performance makes it a powerful tool for bioinformatics. However, existing algorithms do not exploit parallelism due to inner dependencies. We introduce a new method to break the dependencies and show how to fully exploit modern multi-core processors.
AVAILABILITY AND IMPLEMENTATION
Source code and applications are available at https://github.com/rwk-unil/parallel_pbwt.
SUPPLEMENTARY INFORMATION
Supplementary data are available at online.
摘要
位置布罗-惠勒变换（PBWT）数据结构允许进行高效的单倍型数据匹配和压缩。其性能使其成为生物信息学的强大工具。然而，由于内部依赖性，现有算法未利用并行性。我们引入了一种新方法来打破依赖性，并展示了如何充分利用现代多核处理器。
可用性和实现
源代码和应用程序可在https://github.com/rwk-unil/parallel_pbwt获取。
补充信息
补充数据可在网上获取。
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