BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues.
BMC Genomics
; 19(1): 390, 2018 May 23.
Article
in En
| MEDLINE
| ID: mdl-29792182
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 WGBS and the MethylationEPIC array at low WGBS depth, suggesting improved WGBS accuracy after imputation.CONCLUSIONS:
Our findings support the use of BoostMe as a preprocessing step for WGBS analysis.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Sulfites
/
Computational Biology
/
DNA Methylation
/
Whole Genome Sequencing
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
BMC Genomics
Journal subject:
GENETICA
Year:
2018
Type:
Article
Affiliation country:
United States