title: Amplicon Indel Hunter Is a Novel Bioinformatics Tool to Detect Large Somatic Insertion/Deletion Mutations in Amplicon-Based Next-Generation Sequencing Data.
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.
authors: Division of Genomic and Molecular Pathology, Department of Pathology, The University of Chicago, Chicago, Illinois; Center for Research Informatics, The University of Chicago, Chicago, Illinois.
authors: Sabah
authors: S
authors: Kadri
authors: Division of Genomic and Molecular Pathology, Department of Pathology, The University of Chicago, Chicago, Illinois.
authors: Chao J
authors: CJ
authors: Zhen
authors: Division of Genomic and Molecular Pathology, Department of Pathology, The University of Chicago, Chicago, Illinois.
authors: Michelle N
authors: MN
authors: Wurst
authors: Division of Genomic and Molecular Pathology, Department of Pathology, The University of Chicago, Chicago, Illinois.
authors: Bradley C
authors: BC
authors: Long
authors: Division of Genomic and Molecular Pathology, Department of Pathology, The University of Chicago, Chicago, Illinois.
authors: Zi-Feng
authors: ZF
authors: Jiang
authors: Division of Genomic and Molecular Pathology, Department of Pathology, The University of Chicago, Chicago, Illinois.
authors: Y Lynn
authors: YL
authors: Wang
authors: Division of Genomic and Molecular Pathology, Department of Pathology, The University of Chicago, Chicago, Illinois.
authors: Larissa V
authors: LV
authors: Furtado
authors: Division of Genomic and Molecular Pathology, Department of Pathology, The University of Chicago, Chicago, Illinois. Electronic address: jsegal5@bsd.uchicago.edu.
authors: Jeremy P
authors: JP
authors: Segal
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D054643:INDEL Mutation / Q000235:genetics*; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software