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

Jennifer D. Hintzsche, William A. Robinson, Aik-Choon Tan

Research output: Contribution to journalReview article

9 Citations (Scopus)

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.

Original languageEnglish
Article number7983236
JournalInternational Journal of Genomics
Volume2016
DOIs
Publication statusPublished - 2016
Externally publishedYes

Fingerprint

Exome
Technology
Informatics
Disease Management
Surveys and Questionnaires
Research Personnel
Mutation

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Pharmaceutical Science

Cite this

A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data. / Hintzsche, Jennifer D.; Robinson, William A.; Tan, Aik-Choon.

In: International Journal of Genomics, Vol. 2016, 7983236, 2016.

Research output: Contribution to journalReview article

Hintzsche, Jennifer D. ; Robinson, William A. ; Tan, Aik-Choon. / A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data. In: International Journal of Genomics. 2016 ; Vol. 2016.
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