TY - JOUR
T1 - The current status and challenges in computational analysis of genomic big data
AU - Qin, Yiming
AU - Yalamanchili, Hari Krishna
AU - Qin, Jing
AU - Yan, Bin
AU - Wang, Junwen
N1 - Funding Information:
We would like to acknowledge the financial support of the Research Grants Council, University Grants Committee, Hong Kong (Grant No. T12-708/12-N , 781511M , 17121414M ) and National Natural Science Foundation of China (Grant No. 91229105 ). We thank Mr. Ken Yip in the lab for editing.
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2015/3/1
Y1 - 2015/3/1
N2 - 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.
AB - 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.
KW - Gene regulatory networks
KW - Genomic big data
KW - Integrative data analysis
KW - Next generation sequencing
KW - OMICS
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U2 - 10.1016/j.bdr.2015.02.005
DO - 10.1016/j.bdr.2015.02.005
M3 - Review article
AN - SCOPUS:84925687964
SN - 2214-5796
VL - 2
SP - 12
EP - 18
JO - Big Data Research
JF - Big Data Research
IS - 1
ER -