ArticleThe Journal of molecular diagnostics : JMD2019
insiM: in silico Mutator Software for Bioinformatics Pipeline Validation of Clinical Next-Generation Sequencing Assays.
Article in The Journal of molecular diagnostics : JMD, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- In silico generation of synthetic cancer genomes using generative AI.Cell genomics · 2025Article
- Four-Year Laboratory Performance of the First College of American Pathologists In Silico Next-Generation Sequencing Bioinformatics Proficiency Testing Surveys.Archives of pathology & laboratory medicine · 2023Article
- Creation of an Expert Curated Variant List for Clinical Genomic Test Development and Validation: A ClinGen and GeT-RM Collaborative Project.The Journal of molecular diagnostics : JMD · 2021Article
- One in seven pathogenic variants can be challenging to detect by NGS: an analysis of 450,000 patients with implications for clinical sensitivity and genetic test implementation.Genetics in medicine : official journal of the American College of Medical Genetics · 2021Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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