Literature DB >> 26319364

Amplicon Indel Hunter Is a Novel Bioinformatics Tool to Detect Large Somatic Insertion/Deletion Mutations in Amplicon-Based Next-Generation Sequencing Data.

Sabah Kadri1, Chao J Zhen2, Michelle N Wurst2, Bradley C Long2, Zi-Feng Jiang2, Y Lynn Wang2, Larissa V Furtado2, Jeremy P Segal3.   

Abstract

Amplicon-based targeted next-generation sequencing assays are used widely to test for clinically relevant somatic mutations in cancer. However, accurate detection of large insertions and deletions (indels) via these assays remains challenging. Sequencing reads that cover these anomalies are, by definition, different from the reference sequence, and lead to variable performance of alignment algorithms. Reads with large indels may be aligned incorrectly, causing incorrect calls, or may remain unmapped and essentially ignored by downstream informatics pipeline sub-processes. Herein, we describe Amplicon Indel Hunter (AIH), a novel large (>5-bp) indel detection method that is reference genome independent and highly sensitive for the identification of somatic indels in amplicon-based, paired-end, next-generation sequencing data. We validated the algorithm for sensitivity and specificity using a set of clinical cancer samples with Clinical Laboratory Improvement Amendment-confirmed indels as well as in silico mutated data sets. The AIH detected 100% of tested large indels with relatively higher mutant allele frequencies compared with a variety of traditional aligners, which showed variably reduced sensitivity and specificity by comparison. The AIH especially outperformed alignment-based methods for detection of all tested FLT3 internal tandem duplications up to 102 bp. Because AIH runs in parallel to traditional alignment-based informatics pathways, it can be readily incorporated into nearly any analysis pipeline for somatic mutation detection in paired-end amplicon-based data.
Copyright © 2015 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

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Year:  2015        PMID: 26319364     DOI: 10.1016/j.jmoldx.2015.06.005

Source DB:  PubMed          Journal:  J Mol Diagn        ISSN: 1525-1578            Impact factor:   5.568


  11 in total

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