Detection of internal exon deletion with exon Del

Yan Guo, Shilin Zhao, Brian D. Lehmann, Quanhu Sheng, Timothy M. Shaver, Thomas P. Stricker, Jennifer A. Pietenpol, Yu Shyr

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Background: Exome sequencing allows researchers to study the human genome in unprecedented detail. Among the many types of variants detectable through exome sequencing, one of the most over looked types of mutation is internal deletion of exons. Internal exon deletions are the absence of consecutive exons in a gene. Such deletions have potentially significant biological meaning, and they are often too short to be considered copy number variation. Therefore, to the need for efficient detection of such deletions using exome sequencing data exists. Results: We present ExonDel, a tool specially designed to detect homozygous exon deletions efficiently. We tested ExonDel on exome sequencing data generated from 16 breast cancer cell lines and identified both novel and known IEDs. Subsequently, we verified our findings using RNAseq and PCR technologies. Further comparisons with multiple sequencing-based CNV tools showed that ExonDel is capable of detecting unique IEDs not found by other CNV tools. Conclusions: ExonDel is an efficient way to screen for novel and known IEDs using exome sequencing data. ExonDel and its source code can be downloaded freely at https://github.com/slzhao/ExonDel.

Original languageEnglish
Article number332
JournalBMC Bioinformatics
Volume15
Issue number1
DOIs
Publication statusPublished - 2014

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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