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Int J Genomics. 2016;2016:7983236. doi: 10.1155/2016/7983236. Epub 2016 Dec 14.

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

Author information

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

Abstract

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.

Conflict of interest statement

The authors declare that there are no competing interests regarding the publication of this paper.

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