Recommendations for the Use of in Silico Approaches for Next-Generation Sequencing Bioinformatic Pipeline Validation
2022; Elsevier BV; Volume: 25; Issue: 1 Linguagem: Inglês
10.1016/j.jmoldx.2022.09.007
ISSN1943-7811
AutoresEric J. Duncavage, Joshua F. Coleman, Monica E. de Baca, Sabah Kadri, Annette Leon, Mark J. Routbort, Somak Roy, Carlos J. Suarez, Chad Vanderbilt, Justin M. Zook,
Tópico(s)Genomics and Phylogenetic Studies
ResumoIn silico approaches for next-generation sequencing (NGS) data modeling have utility in the clinical laboratory as a tool for clinical assay validation. In silico NGS data can take a variety of forms, including pure simulated data or manipulated data files in which variants are inserted into existing data files. In silico data enable simulation of a range of variants that may be difficult to obtain from a single physical sample. Such data allow laboratories to more accurately test the performance of clinical bioinformatics pipelines without sequencing additional cases. For example, clinical laboratories may use in silico data to simulate low variant allele fraction variants to test the analytical sensitivity of variant calling software or simulate a range of insertion/deletion sizes to determine the performance of insertion/deletion calling software. In this article, the Working Group reviews the different types of in silico data with their strengths and limitations, methods to generate in silico data, and how data can be used in the clinical molecular diagnostic laboratory. Survey data indicate how in silico NGS data are currently being used. Finally, potential applications for which in silico data may become useful in the future are presented. In silico approaches for next-generation sequencing (NGS) data modeling have utility in the clinical laboratory as a tool for clinical assay validation. In silico NGS data can take a variety of forms, including pure simulated data or manipulated data files in which variants are inserted into existing data files. In silico data enable simulation of a range of variants that may be difficult to obtain from a single physical sample. Such data allow laboratories to more accurately test the performance of clinical bioinformatics pipelines without sequencing additional cases. For example, clinical laboratories may use in silico data to simulate low variant allele fraction variants to test the analytical sensitivity of variant calling software or simulate a range of insertion/deletion sizes to determine the performance of insertion/deletion calling software. In this article, the Working Group reviews the different types of in silico data with their strengths and limitations, methods to generate in silico data, and how data can be used in the clinical molecular diagnostic laboratory. Survey data indicate how in silico NGS data are currently being used. Finally, potential applications for which in silico data may become useful in the future are presented. Next-generation sequencing (NGS)–based molecular diagnostics have rapidly proliferated for both molecular somatic and germline applications, necessitating standards and guidelines for their commonplace use in the clinical laboratory.1Richards S. Aziz N. Bale S. Bick D. Das S. Gastier-Foster J. Grody W.W. 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Standards and guidelines for validating next-generation sequencing bioinformatics pipelines.J Mol Diagn. 2018; 20: 4-27Abstract Full Text Full Text PDF PubMed Scopus (246) Google Scholar A major advantage of broad NGS-based panels is that NGS can identify variants in a large number of genes; however, the analytical validation of NGS panels can be challenging, as it is difficult to obtain physical samples with the vast number of variants capable of being detected by the assay. Instead, most laboratories will sequence a representative number of samples or cell lines with known variants and rely on sequencing metrics (ie, the fraction of targets with sufficient coverage) to infer performance across most sequenced positions as part of analytical assay validation.4Jennings L.J. Arcila M.E. Corless C. Kamel-Reid S. Lubin I.M. Pfeifer J. Temple-Smolkin R.L. Voelkerding K.V. Nikiforova M.N. 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A model study of in silico proficiency testing for clinical next-generation sequencing.Arch Pathol Lab Med. 2016; 140: 1085-1091Crossref PubMed Scopus (25) Google Scholar Although many previous guidelines do not discuss in silico data, two previous guidelines for validation of oncology NGS panels and bioinformatics pipelines recommended using in silico data during the optimization and familiarization process and envisioned increasing use of in silico data to augment real samples for some pathogenic mutations.3Roy S. Coldren C. Karunamurthy A. Kip N.S. Klee E.W. Lincoln S.E. Leon A. Pullambhatla M. Temple-Smolkin R.L. Voelkerding K.V. Wang C. Carter A.B. Standards and guidelines for validating next-generation sequencing bioinformatics pipelines.J Mol Diagn. 2018; 20: 4-27Abstract Full Text Full Text PDF PubMed Scopus (246) Google Scholar,4Jennings L.J. Arcila M.E. Corless C. Kamel-Reid S. Lubin I.M. Pfeifer J. Temple-Smolkin R.L. Voelkerding K.V. Nikiforova M.N. Guidelines for validation of next-generation sequencing–based oncology panels: a joint consensus recommendation of the Association for Molecular Pathology and College of American Pathologists.J Mol Diagn. 2017; 19: 341-365Abstract Full Text Full Text PDF PubMed Scopus (406) Google Scholar In this article, the Working Group focuses on what constitutes in silico NGS testing, how in silico data files can be generated, how in silico testing can be used in clinical NGS assay validation, and the limitations of in silico testing compared with physical samples. In silico NGS data may be broadly defined as any data that have been artificially manipulated or generated. For example, in silico data may be generated de novo by simulating reads from reference sequence data (purely simulated data) (Figure 1A and Supplemental Table S1).11Huang W. Li L. Myers J.R. Marth G.T. 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FASTQSim: platform-independent data characterization and in silico read generation for NGS datasets.BMC Res Notes. 2014; 7: 533Crossref PubMed Scopus (26) Google Scholar Finally, in silico approaches may be used when changes are made only to the bioinformatics pipeline and not to the wet laboratory components of the assay; in this scenario, data previously generated by the laboratory are reanalyzed with the new version of the bioinformatics pipeline (data reanalysis) (Figure 1E).Table 1Bioinformatics Tools that Produce Manipulated Assay DataNameGitHub URL∗These are examples of software packages available at the time of this writing and not comprehensive lists. Inclusion does not represent an organizational endorsement by the Association for Molecular Pathology of any individual product or service. All websites last accessed April 12, 2022.SummaryBamgineer (no version given, last update July 30, 2020)https://github.com/pughlab/bamgineerSimulates haplotype-phased, allele-specific copy number variants in an existing BAM file. Requires legacy dependencies samtools version 1.2 and pysam version 0.8.4.41Samadian S. Bruce J.P. Pugh T.J. Bamgineer: introduction of simulated allele-specific copy number variants into exome and targeted sequence data sets.PLoS Comput Biol. 2018; 14: e1006080Crossref PubMed Scopus (4) Google ScholarBAMSurgeon version 1.3https://github.com/adamewing/bamsurgeon/Simulates SNVs, small indels, and structural variants in an existing BAM file.9Ewing A.D. Houlahan K.E. Hu Y. Ellrott K. Caloian C. Yamaguchi T.N. Bare J.C. P'Ng C. Waggott D. Sabelnykova V.Y. Kellen M.R. Norman T.C. Haussler D. Friend S.H. Stolovitzky G. Margolin A.A. Stuart J.M. Boutros P.C. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.Nat Methods. 2015; 12: 623-630Crossref PubMed Scopus (192) Google ScholarinsiM version 1.0https://github.com/thesushantpatil/insiMSimulates SNVs, small indels, and duplication events in an existing BAM file. Outputs a paired-end FASTQ file.42Patil S.A. Mujacic I. Ritterhouse L.L. Segal J.P. Kadri S. insiM: in silico mutator software for bioinformatics pipeline validation of clinical next-generation sequencing assays.J Mol Diagn. 2019; 21: 19-26Abstract Full Text Full Text PDF PubMed Scopus (7) Google ScholarVarBen (no version given, last update May 15, 2021)https://github.com/nccl-jmli/VarBenSimulates SNVs, indels, and structural variants in an existing BAM file.43Li Z. Fang S. Zhang R. Yu L. Zhang J. Bu D. Sun L. Zhao Y. Li J. VarBen: generating in silico reference data sets for clinical next-generation sequencing bioinformatics pipeline evaluation.J Mol Diagn. 2021; 23: 285-299Abstract Full Text Full Text PDF PubMed Scopus (5) Google ScholarIndel, insertion/deletion; SNV, single-nucleotide variant.∗ These are examples of software packages available at the time of this writing and not comprehensive lists. Inclusion does not represent an organizational endorsement by the Association for Molecular Pathology of any individual product or service. All websites last accessed April 12, 2022. Open table in a new tab Indel, insertion/deletion; SNV, single-nucleotide variant. To better assess the technical aspects, limitations, and advantages of in silico variant simulation in the clinical laboratory, the Association for Molecular Pathology (AMP) convened a panel of subject matter experts to examine the topic. In this article, the Working Group explores the different types of in silico data files, how they can be used by clinical laboratories, and their advantages and disadvantages. Data from an Association for Molecular Pathology survey reflects how in silico files are being used by laboratories today. Finally, the Working Group provides recommendations and future directions for the use of in silico NGS data. In silico data can be a powerful tool, but it is important to understand the strengths and limitations of the variety of types of in silico data and which aspects of the pipeline they can help validate. Different types of data will be useful for identifying different sources of errors (eg, systematic sequencing errors versus alignment errors), mimicking different types of variants (eg, small variants versus structural variants), and testing different variant origins (eg, germline versus acquired/somatic). When a change is only made to the bioinformatics pip
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