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Review
. 2017 Feb 15;18(2):412.
doi: 10.3390/ijms18020412.

Big Data Analytics for Genomic Medicine

Karen Y He  1 , Dongliang Ge  2 , Max M He  3   4
Affiliations
Review

Big Data Analytics for Genomic Medicine

Karen Y He et al. Int J Mol Sci. .

Abstract

Genomic medicine attempts to build individualized strategies for diagnostic or therapeutic decision-making by utilizing patients' genomic information. Big Data analytics uncovers hidden patterns, unknown correlations, and other insights through examining large-scale various data sets. While integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a Big Data infrastructure exhibit challenges, they also provide a feasible opportunity to develop an efficient and effective approach to identify clinically actionable genetic variants for individualized diagnosis and therapy. In this paper, we review the challenges of manipulating large-scale next-generation sequencing (NGS) data and diverse clinical data derived from the EHRs for genomic medicine. We introduce possible solutions for different challenges in manipulating, managing, and analyzing genomic and clinical data to implement genomic medicine. Additionally, we also present a practical Big Data toolset for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

Keywords: Big Data analytics; clinically actionable genetic variants; electronic health records; healthcare; next-generation sequencing.

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Conflict of interest statement

Dongliang Ge and Max M. He are employed and may hold stock of and/or stock options with BioSciKin Co., Ltd. This does not alter our adherence to the journal’s policies. The other authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The SRA database growth in the past eight years.
Figure 2
Figure 2
The approximately files sizes of different NGS data formats and running times of generating those different format files. BWA: Burrows-Wheeler aligner, GATAK: genome analysis toolkit, BAM: the binary version of sequence alignment/map, FASTQ: a text-based format for representing either nucleotide sequences or peptide sequences, VCF: variant call format.
Figure 3
Figure 3
The basic framework of SeqHBase for identifying clinically actionable genetic variants.
See this image and copyright information in PMC

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