The Current Status and Challenges in Computational Analysis of Genomic Big Data

被引:24
|
作者
Qin, Yiming [1 ,2 ]
Yalamanchili, Hari Krishna [1 ,2 ]
Qin, Jing [1 ,2 ,3 ]
Yan, Bin [4 ,5 ,6 ]
Wang, Junwen [1 ,2 ,3 ]
机构
[1] Univ Hong Kong, Ctr Genom Sci, LKS Fac Med, Hong Kong, Hong Kong, Peoples R China
[2] Univ Hong Kong, Dept Biochem, LKS Fac Med, Hong Kong, Hong Kong, Peoples R China
[3] Univ Hong Kong, Shenzhen Inst Res & Innovat, Shenzhen, Peoples R China
[4] Univ Hong Kong, Stem Cell & Regenerat Med Consortium, LKS Fac Med, Hong Kong, Hong Kong, Peoples R China
[5] Univ Hong Kong, Dept Physiol, Hong Kong, Hong Kong, Peoples R China
[6] Chinese Acad Sci, Shenzhen Inst Adv Technol, Lab Food Safety & Environm Technol, Shenzhen, Peoples R China
基金
中国国家自然科学基金;
关键词
Gene regulatory networks; Next generation sequencing; OMICS; Integrative data analysis; Genomic big data;
D O I
10.1016/j.bdr.2015.02.005
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
DNA, RNA and protein are three major kinds of biological macromolecules with up to billions of basic elements in such biological organisms as human or mouse. They function at molecular, cellular and organismal levels individually and interactively. Traditional assays on such macromolecules are largely experimentally based, which are usually time consuming and laborious. In the past few years, high-throughput technologies, such as microarray and next-generation sequencing (NGS), were developed. Consequently, large genomic datasets are being generated and computational tools to analyzing these data are in urgent demand. This paper reviews several state-of-the-art high-throughput methodologies, representative projects, available databases and bioinformatics tools at different molecular levels. Finally, challenges and perspectives in processing genomic big data are discussed. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码:12 / 18
页数:7
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