Ideafix: a decision tree-based method for the refinement of variants in FFPE DNA sequencing data.

ABSTRACT: Increasingly, treatment decisions for cancer patients are being made from next-generation sequencing results generated from formalin-fixed and paraffin-embedded (FFPE) biopsies. However, this material is prone to sequence artefacts that cannot be easily identified. In order to address this issue, we designed a machine learning-based algorithm to identify these artefacts using data from >1 600 000 variants from 27 paired FFPE and fresh-frozen breast cancer samples. Using these data, we assembled a series of variant features and evaluated the classification performance of five machine learning algorithms. Using leave-one-sample-out cross-validation, we found that XGBoost (extreme gradient boosting) and random forest obtained AUC (area under the receiver operating characteristic curve) values

SUBMITTER: Tellaetxe-Abete M 

PROVIDER: S-EPMC8557387 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature