Parallel computing for genome sequence processing

You Zou; Yuejie Zhu; Yaohang Li; Fang-Xiang Wu; Jianxin Wang

doi:10.1093/bib/bbab070

Parallel computing for genome sequence processing

Brief Bioinform. 2021 Sep 2;22(5):bbab070. doi: 10.1093/bib/bbab070.

Authors

You Zou¹, Yuejie Zhu¹, Yaohang Li², Fang-Xiang Wu³, Jianxin Wang⁴

Affiliations

¹ Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering at Central South University, Changsha, China.
² computer science at Old Dominion University, USA.
³ College of Engineering and the Department of Computer Science at the University of Saskatchewan, Saskatoon, Canada.
⁴ School of Computer Science and Engineering at Central South University, Changsha, Hunan, China.

PMID: 33822883
DOI: 10.1093/bib/bbab070

Abstract

The rapid increase of genome data brought by gene sequencing technologies poses a massive challenge to data processing. To solve the problems caused by enormous data and complex computing requirements, researchers have proposed many methods and tools which can be divided into three types: big data storage, efficient algorithm design and parallel computing. The purpose of this review is to investigate popular parallel programming technologies for genome sequence processing. Three common parallel computing models are introduced according to their hardware architectures, and each of which is classified into two or three types and is further analyzed with their features. Then, the parallel computing for genome sequence processing is discussed with four common applications: genome sequence alignment, single nucleotide polymorphism calling, genome sequence preprocessing, and pattern detection and searching. For each kind of application, its background is firstly introduced, and then a list of tools or algorithms are summarized in the aspects of principle, hardware platform and computing efficiency. The programming model of each hardware and application provides a reference for researchers to choose high-performance computing tools. Finally, we discuss the limitations and future trends of parallel computing technologies.

Keywords: cluster computing; genome sequence processing; heterogeneous computing; multi-core computing; parallel computing.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Algorithms
Base Sequence / genetics
Chromosome Mapping / methods
Electronic Data Processing / methods*
Genome, Human*
Genomics / methods*
High-Throughput Nucleotide Sequencing / methods
Humans
Information Storage and Retrieval
Polymorphism, Single Nucleotide*
Sequence Alignment / methods*
Software
Whole Genome Sequencing / methods