alexa Identification of Insertion Deletion Mutations from Dee
ISSN: 2153-0602

Journal of Data Mining in Genomics & Proteomics
Open Access

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Research Article

Identification of Insertion Deletion Mutations from Deep Targeted Resequencing

Georges Natsoulis1, Nancy Zhang2, Katrina Welch3, John Bell3 and Hanlee P Ji1,3*

1Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA

2Department of Statistics, University of Pennsylvania, Philadelphia, PA, 19104, USA

3Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94306, USA

These authors contributed equally to this work

*Corresponding Author:
Hanlee P Ji
Division of Oncology
Department of Medicine–Stanford University School of Medicine CCSR 1115
Stanford, CA 94305-5151, USA
Tel: 650-721-1503
Fax: 650-725-1420
E-mail: [email protected]

Received date: May 07, 2013; Accepted date: June 24, 2013; Published date: July 02, 2013

Citation: Natsoulis G, Zhang N, Welch K, Bell J, Ji HP (2013) Identification of Insertion Deletion Mutations from Deep Targeted Resequencing. J Data Mining Genomics Proteomics 4:132. doi:10.4172/2153-0602.1000132

Copyright: © 2013 Natsoulis G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited



Taking advantage of the deep targeted sequencing capabilities of next generation sequencers, we have developed a novel two step insertion deletion (indel) detection algorithm (IDA) that can determine indels from single read sequences with high computational efficiency and sensitivity when indels are fractionally less compared to wild type reference sequence. First, it identifies candidate indel positions utilizing specific sequence alignment artifacts produced by rapid alignment programs. Second, it confirms the location of the candidate indel by using the Smith-Waterman (SW) algorithm on a restricted subset of Sequence reads. We demonstrate that IDA is applicable to indels of varying sizes from deep targeted sequencing data at low fractions where the indel is diluted by wild type sequence. Our algorithm is useful in detecting indel variants present at variable allelic frequencies such as may occur in heterozygotes and mixed normal-tumor tissue.

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