Academic Journal
BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues
| Title: | BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues |
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| Authors: | Luli S. Zou, Michael R. Erdos, D. Leland Taylor, Peter S. Chines, Arushi Varshney, The McDonnell Genome Institute, Stephen C. J. Parker, Francis S. Collins, John P. Didion |
| Source: | BMC Genomics, Vol 19, Iss 1, Pp 1-15 (2018) |
| Publisher Information: | BMC, 2018. |
| Publication Year: | 2018 |
| Collection: | LCC:Biotechnology LCC:Genetics |
| Subject Terms: | DNA methylation, XGBoost, Whole-genome bisulfite sequencing (WGBS), EPIC, Imputation, Adipose, Biotechnology, TP248.13-248.65, Genetics, QH426-470 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1471-2164 |
| Relation: | http://link.springer.com/article/10.1186/s12864-018-4766-y; https://doaj.org/toc/1471-2164 |
| DOI: | 10.1186/s12864-018-4766-y |
| Access URL: | https://doaj.org/article/ca98178c6bb4404ca884764b5ac2aaff |
| Accession Number: | edsdoj.98178c6bb4404ca884764b5ac2aaff |
| Database: | Directory of Open Access Journals |
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| ISSN: | 14712164 |
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| DOI: | 10.1186/s12864-018-4766-y |
| Published in: | BMC Genomics |
| Language: | English |