Detection of internal exon deletion with exon Del

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• 作者：Yan Guo (29)
  Shilin Zhao (29)
  Brian D Lehmann (30)
  Quanhu Sheng (29)
  Timothy M Shaver (29)
  Thomas P Stricker (31)
  Jennifer A Pietenpol (30)
  Yu Shyr (29)
  
  29. Vanderbilt Ingram Cancer Center ; Center for Quantitative Sciences ; 2220 Pierce Ave ; 549 Preston Research Building ; Nashville ; TN ; 37232 ; USA
  30. Department of Biochemistry ; Vanderbilt University ; Nashville ; TN ; 37232 ; USA
  31. Department of Pathology ; Vanderbilt University ; Nashville ; TN ; 37232 ; USA
  
• 刊名：BMC Bioinformatics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：767 KB
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• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105

文摘

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.