Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing.

Published online

Journal Article (Review)

The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, pre-processing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.

Full Text

Duke Authors

Cited Authors

  • Bao, R; Huang, L; Andrade, J; Tan, W; Kibbe, WA; Jiang, H; Feng, G

Published Date

  • 2014

Published In

Volume / Issue

  • 13 / Suppl 2

Start / End Page

  • 67 - 82

PubMed ID

  • 25288881

Pubmed Central ID

  • 25288881

International Standard Serial Number (ISSN)

  • 1176-9351

Digital Object Identifier (DOI)

  • 10.4137/CIN.S13779


  • eng

Conference Location

  • United States