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Identification of Insertion Deletion Mutations from Deep Targeted Resequencing
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3917607/ https://www.ncbi.nlm.nih.gov/pubmed/24511426 http://dx.doi.org/10.4172/2153-0602.1000132 |
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author | Natsoulis, Georges Zhang, Nancy Welch, Katrina Bell, John Ji, Hanlee P |
author_facet | Natsoulis, Georges Zhang, Nancy Welch, Katrina Bell, John Ji, Hanlee P |
author_sort | Natsoulis, Georges |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3917607 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
record_format | MEDLINE/PubMed |
spelling | pubmed-39176072014-02-07 Identification of Insertion Deletion Mutations from Deep Targeted Resequencing Natsoulis, Georges Zhang, Nancy Welch, Katrina Bell, John Ji, Hanlee P J Data Mining Genomics Proteomics Article 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. 2013-07-02 /pmc/articles/PMC3917607/ /pubmed/24511426 http://dx.doi.org/10.4172/2153-0602.1000132 Text en Copyright: © 2013 Natsoulis G, et al. http://creativecommons.org/licenses/by/2.0/ 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. |
spellingShingle | Article Natsoulis, Georges Zhang, Nancy Welch, Katrina Bell, John Ji, Hanlee P Identification of Insertion Deletion Mutations from Deep Targeted Resequencing |
title | Identification of Insertion Deletion Mutations from Deep Targeted Resequencing |
title_full | Identification of Insertion Deletion Mutations from Deep Targeted Resequencing |
title_fullStr | Identification of Insertion Deletion Mutations from Deep Targeted Resequencing |
title_full_unstemmed | Identification of Insertion Deletion Mutations from Deep Targeted Resequencing |
title_short | Identification of Insertion Deletion Mutations from Deep Targeted Resequencing |
title_sort | identification of insertion deletion mutations from deep targeted resequencing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3917607/ https://www.ncbi.nlm.nih.gov/pubmed/24511426 http://dx.doi.org/10.4172/2153-0602.1000132 |
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