| Autor: |
Duncavage, Eric J., Coleman, Joshua F., de Baca, Monica E., Kadri, Sabah, Leon, Annette, Routbort, Mark, Roy, Somak, Suarez, Carlos J., Vanderbilt, Chad, Zook, Justin M. |
| Zdroj: |
The Journal of Molecular Diagnostics; January 2023, Vol. 25 Issue: 1 p3-16, 14p |
| Abstrakt: |
In silicoapproaches for next-generation sequencing (NGS) data modeling have utility in the clinical laboratory as a tool for clinical assay validation. In silicoNGS 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 silicodata 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 silicodata 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 silicodata with their strengths and limitations, methods to generate in silicodata, and how data can be used in the clinical molecular diagnostic laboratory. Survey data indicate how in silicoNGS data are currently being used. Finally, potential applications for which in silicodata may become useful in the future are presented. |
| Databáze: |
Supplemental Index |
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