Pindel

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


References in zbMATH (referenced in 4 articles )

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  1. Kim, Jongkyu; Reinert, Knut: Vaquita: fast and accurate identification of structural variation using combined evidence (2017)
  2. Zhang, Nancy R.; Yakir, Benjamin; Xia, Li C.; Siegmund, David: Scan statistics on Poisson random fields with applications in genomics (2016)
  3. Ryan M. Layer, Ira M. Hall, Aaron R. Quinlan: LUMPY: A probabilistic framework for structural variant discovery (2012) arXiv
  4. Ye, Kai; Schulz, Marcel H.; Long, Quan; Apweiler, Rolf; Ning, Zemin: Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads (2009) ioport