parSRA: A framework for the parallel execution of short read aligners on compute clusters

被引：2

作者：

Gonzalez-Dominguez, Jorge ^{[1
]}

Hundt, Christian ^{[2
]}

Schmidt, Bertil ^{[2
]}

机构：

[1] Univ A Coruna, Comp Architecture Grp, La Coruna, Spain

[2] Johannes Gutenberg Univ Mainz, Parallel & Distributed Architectures Grp, Mainz, Germany

来源：

JOURNAL OF COMPUTATIONAL SCIENCE | 2018年 / 25卷

关键词：

Short read alignment; High performance computing; Multicore clusters; Bioinformatics; PGAS; ALIGNMENT; ACCURATE; TOOL;

D O I：

10.1016/j.jocs.2017.01.008

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The growth of next generation sequencing datasets poses as a challenge to the alignment of reads to reference genomes in terms of both accuracy and speed. In this work we present parSRA, a parallel framework to accelerate the execution of existing short read aligners on distributed-memory systems. parSRA can be used to parallelize a variety of short read alignment tools installed in the system without any modification to their source code. We show that our framework provides good scalability on a compute cluster for accelerating the popular BWA-MEM and Bowtie2 aligners. On average, it is able to accelerate sequence alignments on 16 64-core nodes (in total, 1024 cores) with speedup of 10.48 compared to the original multithreaded tools running with 64 threads on one node. It is also faster and more scalable than the pMap and BigBWA frameworks. Source code of parSRA in C++ and UPC++ running on Linux systems with support for FUSE is freely available at https://sourceforge.net/projects/parsra/. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：134 / 139

页数：6

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