Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads.
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.
Ye
Kai
K
EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. k.ye@lumc.nl
Schulz
Marcel H
MH
Long
Quan
Q
Apweiler
Rolf
R
Ning
Zemin
Z
D000465:Algorithms; D056905:Chromosome Breakpoints; D019295:Computational Biology / Q000379:methods*; D053960:DNA Breaks; D016678:Genome; D054643:INDEL Mutation; D017422:Sequence Analysis, DNA; D012984:Software