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Bioinformatics Advance Access published online on June 26, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp394
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads

Kai Ye 1,2,*, Marcel H. Schulz 1,4, Quan Long 3, Rolf Apweiler 1 and Zemin Ning 3

1EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
2Departments of Molecular Epidemiology, Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
3The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
4Max Planck Institute for Molecular Genetics and International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany.

*To whom correspondence should be addressed. Dr. Kai Ye, E-mail: kye{at}ebi.ac.uk


  Abstract

Motivation: 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.

Availability: The binary code and a short user manual can be freely downloaded from http://www.ebi.ac.uk/~kye/pindel/.

Associate Editor: Prof. Alfonso Valencia

Received on April 27, 2009; revised on June 20, 2009; accepted on June 21, 2009

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