Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

doi:10.1016/j.csbj.2017.07.004

	Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges
	Yin, Zekun 1; Lan, Haidong 1; Tan, Guangming 2; Lu, Mian 3; Vasilakos, Athanasios V.4; Liu, Weiguo 1
	2017
发表期刊	COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
ISSN	2001-0370
卷号	15 页码:403-411
摘要	The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics. (C) 2017 The Authors. Published by Elsevier B.V.
关键词	Computational biology applications Computing platforms Big biological data NGS GPU Intel MIC
DOI	10.1016/j.csbj.2017.07.004
收录类别	SCI
语种	英语
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
WOS类目	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology
WOS记录号	WOS:000425900600030
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：38[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/6151
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Weiguo
作者单位	1.Shandong Univ, Jinan, Shandong, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Huawei Singapore Res Ctr, Singapore, Singapore 4.Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, SE-93187 Skelleftea, Sweden
推荐引用方式 GB/T 7714	Yin, Zekun,Lan, Haidong,Tan, Guangming,et al. Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges[J]. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,2017,15:403-411.
APA	Yin, Zekun,Lan, Haidong,Tan, Guangming,Lu, Mian,Vasilakos, Athanasios V.,&Liu, Weiguo.(2017).Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL,15,403-411.
MLA	Yin, Zekun,et al."Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges".COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 15(2017):403-411.