A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.

ABSTRACT: BACKGROUND:Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist. For this reason, many clinical laboratories confirm NGS results using orthogonal technologies such as Sanger sequencing. Here, we report the development of a deterministic machine-learning-based model to differentiate between these two types of variant calls: those that do not require confirmation using

SUBMITTER: van den Akker J 

PROVIDER: S-EPMC5904977 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature