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Open Access Highly Accessed Methodology article

Accurate indel prediction using paired-end short reads

Dominik Grimm1*, Jörg Hagmann2, Daniel Koenig2, Detlef Weigel2 and Karsten Borgwardt13

Author affiliations

1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology and Max Planck Institute for Intelligent Systems, Tübingen, Germany

2 Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany

3 Center for Bioinformatics, Eberhard Karls Universität, Tübingen, Germany

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Citation and License

BMC Genomics 2013, 14:132  doi:10.1186/1471-2164-14-132

Published: 27 February 2013

Abstract

Background

One of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives.

Results

Here, we present a machine learning based method which is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. Our method identifies indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. We demonstrate the usefulness of our method with paired-end Illumina reads from 80 genomes of the first phase of the 1001 Genomes Project ( http://www.1001genomes.org webcite) in Arabidopsis thaliana.

Conclusion

In this work we show that indel classification is a necessary step to reduce the number of false positive candidates. We demonstrate that missing classification may lead to spurious biological interpretations. The software is available at: http://agkb.is.tuebingen.mpg.de/Forschung/SV-M/ webcite.

Keywords:
Next generation sequencing; Indel detection; Discriminative machine learning; Paired-end short reads; Split-read mapping