Analysis of machine learning algorithms as integrative tools for validation of next generation sequencing data.
Journal:
European review for medical and pharmacological sciences
Published Date:
Sep 1, 2019
Abstract
OBJECTIVE: While next generation sequencing (NGS) has become the technology of choice for clinical diagnostics, most genetic laboratories still use Sanger sequencing for orthogonal confirmation of NGS results. Previous studies have shown that when the quality of NGS data is high, most calls are indicated by Sanger sequencing, making confirmation redundant. We aimed at establishing a set of criteria that make it possible to distinguish NGS calls that need orthogonal confirmation from those that do not would significantly decrease the amount of work necessary to reach a diagnosis.