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Accurate prediction of single-cell DNA methylation states using deep learning

View ORCID ProfileChristof Angermueller, View ORCID ProfileHeather J. Lee, View ORCID ProfileWolf Reik, View ORCID ProfileOliver Stegle
doi: https://doi.org/10.1101/055715
Christof Angermueller
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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  • ORCID record for Christof Angermueller
Heather J. Lee
2Epigenetics Programme, Babraham Institute, Cambridge, UK
3Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
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Wolf Reik
2Epigenetics Programme, Babraham Institute, Cambridge, UK
3Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
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Oliver Stegle
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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Abstract

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

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Posted February 01, 2017.
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Accurate prediction of single-cell DNA methylation states using deep learning
Christof Angermueller, Heather J. Lee, Wolf Reik, Oliver Stegle
bioRxiv 055715; doi: https://doi.org/10.1101/055715
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Accurate prediction of single-cell DNA methylation states using deep learning
Christof Angermueller, Heather J. Lee, Wolf Reik, Oliver Stegle
bioRxiv 055715; doi: https://doi.org/10.1101/055715

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