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International Journal of Genomics
Volume 2016 (2016), Article ID 7983236, 16 pages
Review Article

A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

1Division of Medical Oncology, Department of Medicine, School of Medicine, Aurora, CO 80045, USA
2University of Colorado Cancer Center, Aurora, CO 80045, USA
3Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA

Received 25 May 2016; Accepted 26 October 2016

Academic Editor: Lam C. Tsoi

Copyright © 2016 Jennifer D. Hintzsche et al. 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.


Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.