A UK team has developed a computational method for filling in gaps in available DNA methylation profiles produced from individual cells.
The researchers used a so-called deep neural network strategy to develop their method, called DeepCpG, as they reported [April 10] in Genome Biology.
“Across all cell types, DeepCpG yielded substantially more accurate predictions of methylation states than previous approaches,” senior author Oliver Stegle, a group leader at the European Bioinformatics Institute’s European Molecular Biology Lab, and his co-authors wrote. DeepCpG “uncovered both previously known and de novo sequence motifs that are associated with methylation changes and methylation variability between cells,” they added.
When they applied DeepCpG to whole-genome, single-cell bisulfite sequence data for 18 mouse embryonic stem cells, for example, the authors found that the approach “yielded more accurate predictions than any of the alternative methods, both genome-wide and in different genomic contexts.”
“Several of the motifs discovered by DeepCpG could be matched to known motifs that are implicated in the regulation of DNA methylation,” the authors wrote.
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