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Vindel: a simple pipeline for checking indel redundancy.

Zhiyi Li1, Xiaowei Wu2, Bin He3

  • 1Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA. zli04@vt.edu.

BMC Bioinformatics
|November 20, 2014
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Summary
This summary is machine-generated.

A new computational pipeline, Vindel, identifies redundant insertion-deletion (indel) variants in the dbSNP database. This tool found nearly 10% of human indels are redundant, highlighting issues in current indel annotation.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) identifies numerous insertion-deletion (indel) variants in human populations.
  • A significant fraction of identified indels may be false positives due to sequencing errors, alignment artifacts, or annotation mistakes.

Purpose of the Study:

  • To examine indel redundancy within the dbSNP database.
  • To develop a computational pipeline (Vindel) for detecting redundant indels.

Main Methods:

  • Vindel utilizes indel position information to group potential redundant variants.
  • It generates indel variant substrings by mutating the reference genome.
  • Pairwise comparison of these substrings identifies redundant indels.

Main Results:

  • The Vindel pipeline detected approximately 8% redundancy in insertion indels and 12% in deletion indels.
  • Overall, 10% of combined insertion and deletion indels in dbSNP were found to be redundant.
  • Redundancy levels were consistent across all human autosomes.

Conclusions:

  • Vindel is an effective tool for identifying redundant indels in databases like dbSNP.
  • Nearly 0.6 million out of 5.9 million examined indels were redundant, indicating a significant annotation limitation.
  • The indel redundancy detection algorithm is available as a standalone pipeline and a web server.