Cloud Computing Enabled Big Multi-Omics Data Analytics

被引:20
|
作者
Koppad, Saraswati [1 ]
Annappa, B. [1 ]
Gkoutos, Georgios, V [2 ,3 ,4 ,5 ,6 ,7 ,8 ]
Acharjee, Animesh [2 ,3 ,4 ,5 ]
机构
[1] Natl Inst Technol Karnataka, Dept Comp Sci & Engn, Surathkal, India
[2] Univ Birmingham, Coll Med & Dent Sci, Inst Canc & Genom Sci, Birmingham B15 2TT, W Midlands, England
[3] Univ Birmingham, Coll Med & Dent Sci, Ctr Computat Biol, Birmingham B15 2TT, W Midlands, England
[4] Univ Hosp Birmingham NHS Fdn Trust, Inst Translat Med, Birmingham, W Midlands, England
[5] Univ Hosp Birmingham, NIHR Surg Reconstruct & Microbiol Res Ctr, Birmingham, W Midlands, England
[6] MRC Hlth Data Res UK HDR UK, London, England
[7] NIHR Expt Canc Med Ctr, Birmingham, W Midlands, England
[8] Univ Hosp Birmingham, NIHR Biomed Res Ctr, Birmingham, W Midlands, England
来源
基金
英国医学研究理事会; 英国科研创新办公室; 欧盟地平线“2020”;
关键词
Big data; cloud computing; multi-omics data; data analytics; data integration; CANCER GENOMICS CLOUD; SEQUENCING DATA; FRAMEWORK; TOOLKIT; COST;
D O I
10.1177/11779322211035921
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications.
引用
收藏
页数:16
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