Statistics Papers
Document Type
Journal Article
Date of this Version
2013
Publication Source
Journal of Data Mining in Genomics & Proteomics
Volume
4
Issue
3
DOI
10.4172/2153-0602.1000132
Abstract
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.
Copyright/Permission Statement
© 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.
Recommended Citation
Natsoulis, G., Zhang, N. R., Welch, K., Bell, J., & Ji, H. P. (2013). Identification of Insertion Deletion Mutations from Deep Targeted Resequencing. Journal of Data Mining in Genomics & Proteomics, 4 (3), http://dx.doi.org/10.4172/2153-0602.1000132
Date Posted: 27 November 2017