Reducing Sanger confirmation testing through false positive prediction algorithms.

ABSTRACT:

Purpose

Clinical genome sequencing (cGS) followed by orthogonal confirmatory testing is standard practice. While orthogonal testing significantly improves specificity, it also results in increased turnaround time and cost of testing. The purpose of this study is to evaluate machine learning models trained to identify false positive variants in cGS data to reduce the need for orthogonal testing.

Methods

We sequenced five reference human genome samples characterized by the Genome in a Bottle Consortium (GIAB) and compared the results with an established set of variants for each genome referred to as a truth set. We then trained machine learning models to identify variants that were labeled as false positives.

Results

After training, the models identified 99.5% of the fa

SUBMITTER: Holt JM 

PROVIDER: S-EPMC8257489 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature