title: Accurate indel prediction using paired-end short reads.
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) in Arabidopsis thaliana.
  
  
  CONCLUSIONS
  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/.
authors: Machine Learning and Computational Biology Research Group, Max Planck Institute for Developmental Biology and Max Planck Institute for Intelligent Systems, Tübingen, Germany. dominik.grimm@tuebingen.mpg.de
authors: Dominik
authors: D
authors: Grimm
authors: Jörg
authors: J
authors: Hagmann
authors: Daniel
authors: D
authors: Koenig
authors: Detlef
authors: D
authors: Weigel
authors: Karsten
authors: K
authors: Borgwardt
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D001185:Artificial Intelligence; D019295:Computational Biology; D059014:High-Throughput Nucleotide Sequencing; D054643:INDEL Mutation / Q000235:genetics*; D012984:Software