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Review
. 2019 Feb 12:10:49.
doi: 10.3389/fgene.2019.00049. eCollection 2019.

Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics

Affiliations
Review

Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics

Pawel Suwinski et al. Front Genet. .

Abstract

There is a growing attention toward personalized medicine. This is led by a fundamental shift from the 'one size fits all' paradigm for treatment of patients with conditions or predisposition to diseases, to one that embraces novel approaches, such as tailored target therapies, to achieve the best possible outcomes. Driven by these, several national and international genome projects have been initiated to reap the benefits of personalized medicine. Exome and targeted sequencing provide a balance between cost and benefit, in contrast to whole genome sequencing (WGS). Whole exome sequencing (WES) targets approximately 3% of the whole genome, which is the basis for protein-coding genes. Nonetheless, it has the characteristics of big data in large deployment. Herein, the application of WES and its relevance in advancing personalized medicine is reviewed. WES is mapped to Big Data "10 Vs" and the resulting challenges discussed. Application of existing biological databases and bioinformatics tools to address the bottleneck in data processing and analysis are presented, including the need for new generation big data analytics for the multi-omics challenges of personalized medicine. This includes the incorporation of artificial intelligence (AI) in the clinical utility landscape of genomic information, and future consideration to create a new frontier toward advancing the field of personalized medicine.

Keywords: analytics; big data; exome; personalized medicine; precision; sequencing.

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Figures

FIGURE 1
FIGURE 1
The 10 Vs big data characteristics of whole exome sequencing.
FIGURE 2
FIGURE 2
The changing paradigms of personalized medicine. (A) Notable timelines in Genomics and Personalized Medicine, including the data storage size for the four big data domains by 2025, with genomics either on par or the most demanding of the domains (Stephens et al., 2015). (B) The intersection of big data analytics and WES for advancement of personalized medicine. The drawings are not to scale.

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