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

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ID: 290932
2009
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Abstract
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/. Contact: k.ye@lumc.nl; zn1@sanger.ac.uk
Reference Key
openalex_W2154397429 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kai Ye, Marcel H. Schulz, Quan Long, Rolf Apweiler, Zemin Ning
Journal BMC Bioinformatics
Year 2009
DOI
10.1093/bioinformatics/btp394
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