insiM: in silico Mutator Software for Bioinformatics Pipeline Validation of Clinical Next-Generation Sequencing Assays
- PMID: 30273779
- DOI: 10.1016/j.jmoldx.2018.08.001
insiM: in silico Mutator Software for Bioinformatics Pipeline Validation of Clinical Next-Generation Sequencing Assays
Abstract
Lack of reliable reference samples containing different mutations of interest across large sets of disease-relevant loci limits the extensive validation clinical next-generation sequencing (NGS) assays and their associated bioinformatics pipelines. Herein, we have generated a publicly available, highly flexible tool, in silico Mutator (insiM), to introduce point mutations, insertions, deletions, and duplications of any size into real data sets of amplicon-based or hybrid-capture NGS assays. insiM accepts an alignment file along with target territory and produces paired-end FASTQ files containing specified mutations via modification of original sequencing reads. Mutant signal is, thus, generated within the context of existing real-world data to most closely mimic assay performance. Resulting files may then be passed through the assay's bioinformatics pipeline to assist with assay/bioinformatics validation and to identify performance gaps in detection. To establish the basic functionality of the software, a series of simulation experiments with varying mutation types, sizes, and allele frequencies were performed across the entire clinical territory of hybrid-capture and amplicon-based clinical assays developed at The University of Chicago. This work demonstrates the utility of insiM as a supplementary tool during the validation of an NGS assay's bioinformatics pipeline.
Copyright © 2019 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.
Comment in
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Expanding the Scope of The Journal of Molecular Diagnostics to the Informatics Subdivision of the Association for Molecular Pathology.J Mol Diagn. 2019 Jul;21(4):539-541. doi: 10.1016/j.jmoldx.2019.04.001. J Mol Diagn. 2019. PMID: 31230765
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