title: Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads.
abstract: BACKGROUND
  There is a strong demand in the genomic community to develop effective algorithms to reliably identify genomic variants. Indel detection using next-gen data is difficult and identification of long structural variations is extremely challenging.
  
  
  RESULTS
  We present Pindel, a pattern growth approach, to detect breakpoints of large deletions and medium-sized insertions from paired-end short reads. We use both simulated reads and real data to demonstrate the efficiency of the computer program and accuracy of the results.
  
  
  BACKGROUND
  The binary code and a short user manual can be freely downloaded from http://www.ebi.ac.uk/ approximately kye/pindel/.
  
  
  BACKGROUND
  k.ye@lumc.nl; zn1@sanger.ac.uk.
authors: EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. k.ye@lumc.nl
authors: Kai
authors: K
authors: Ye
authors: Marcel H
authors: MH
authors: Schulz
authors: Quan
authors: Q
authors: Long
authors: Rolf
authors: R
authors: Apweiler
authors: Zemin
authors: Z
authors: Ning
mesh_terms: D000465:Algorithms; D056905:Chromosome Breakpoints; D019295:Computational Biology / Q000379:methods*; D053960:DNA Breaks; D016678:Genome; D054643:INDEL Mutation; D017422:Sequence Analysis, DNA; D012984:Software