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Genome Biol. 2017 Apr 11;18(1):67. doi: 10.1186/s13059-017-1189-z.

DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. cangermueller@ebi.ac.uk.
2
Epigenetics Programme, Babraham Institute, Cambridge, UK.
3
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
4
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. oliver.stegle@ebi.ac.uk.

Abstract

Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells. We evaluate DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols. DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability.

KEYWORDS:

Artificial neural network; DNA methylation; Deep learning; Epigenetics; Machine learning; Single-cell genomics

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