期刊
GENOME BIOLOGY
卷 18, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s13059-017-1189-z
关键词
Deep learning; Artificial neural network; Machine learning; Single-cell genomics; DNA methylation; Epigenetics
资金
- European Molecular Biology Laboratory
- European Union [N635290]
- European Molecular Biology Laboratory (EMBL)
- Wellcome Trust
- European Union
- UK Biotechnology and Biological Sciences Research Council (BBSRC)
- EU
- BBSRC [BBS/E/B/0000S266, BBS/E/B/0000H334, BBS/E/B/000C0403] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BBS/E/B/000C0403, BBS/E/B/0000S266, BBS/E/B/0000H334] Funding Source: researchfish
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.
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