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Microindel detection in short-read sequence data.

Peter Krawitz1, Christian Rödelsperger, Marten Jäger

  • 1Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin. peter.krawitz@googlemail.com

Bioinformatics (Oxford, England)
|February 11, 2010
PubMed
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Detecting microindels in short-read sequencing data is a bioinformatics challenge. This study shows a >90% detection rate for microindels using gapped alignments, improving single nucleotide variant (SNV) detection.

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Deep short-read sequencing effectively detects single nucleotide variants (SNVs).
  • Automated microindel detection in short-read data remains a bioinformatics challenge.
  • Existing SNV detection algorithms struggle with microindel identification.

Purpose of the Study:

  • To evaluate short-read mapping tools for indel detection.
  • To assess the impact of indels on SNV detection error rates.
  • To develop and validate an algorithm for microindel calling from short-read data.

Main Methods:

  • Systematic analysis of mapping tools (MAQ, Bowtie, BWA, Novoalign, RazerS) on simulated indel datasets.
  • Implementation of an equivalent indel region (eir) algorithm for indel calling.

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  • Evaluation of microindel detection rates and comparison with Sanger and 454 platforms.

    • Main Results:

    • Microindel detection rate for <4 bp indels exceeds 90% on simulated short-read data.
    • Gapped alignments reduce systematic errors in SNV detection.
    • Microindel detection from short reads identifies both overlapping and distinct indels compared to other platforms.

    Conclusions:

    • Gapped alignments are crucial for accurate SNV detection and enable reliable microindel calling from short-read data.
    • The developed algorithm demonstrates high efficacy for microindel detection.
    • Short-read sequencing, with appropriate algorithms, is a viable method for comprehensive variant detection, including microindels.