BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues.

ABSTRACT:

Background

Bisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to coverage depth variability. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features associated with predictive power.

Results

Here we describe BoostMe, a method for imputing low-quality DNA methylation estimates within whole-genome bisulfite sequencing (WGBS) data. BoostMe uses a gradient boosting algorithm, XGBoost, and leverages information from multiple samples for prediction. We find that BoostMe outperforms existing algorithms in speed and accuracy when applied to WGBS of human tissues. Furthermore, we show that imputation improves concordance between WGB

SUBMITTER: Zou LS 

PROVIDER: S-EPMC5966887 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature