Next Article in Journal
The Search for Dietary Supplements to Elevate or Activate Circulating Paraoxonases
Next Article in Special Issue
Bioinformatics Approaches for Fetal DNA Fraction Estimation in Noninvasive Prenatal Testing
Previous Article in Journal
Biofilm Formation and Immunomodulatory Activity of Proteus mirabilis Clinically Isolated Strains
Previous Article in Special Issue
Expression of Iron-Related Proteins Differentiate Non-Cancerous and Cancerous Breast Tumors
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessReview

Big Data Analytics for Genomic Medicine

Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA
BioSciKin Co., Ltd., Nanjing 210042, China
Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA
Authors to whom correspondence should be addressed.
Academic Editor: William Chi-shing Cho
Int. J. Mol. Sci. 2017, 18(2), 412;
Received: 24 October 2016 / Revised: 8 February 2017 / Accepted: 9 February 2017 / Published: 15 February 2017
(This article belongs to the Special Issue Precision Medicine—From Bench to Bedside)
PDF [1212 KB, uploaded 15 February 2017]


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

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

He, K.Y.; Ge, D.; He, M.M. Big Data Analytics for Genomic Medicine. Int. J. Mol. Sci. 2017, 18, 412.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top