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Big Data Analytics for Genomic Medicine
#MMPMID28212287
He KY
; Ge D
; He MM
Int J Mol Sci
2017[Feb]; 18
(2
): ä PMID28212287
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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.
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