Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning

The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility between well characterized reference cell types and a query cellular context, and copies over signal of transcription factor binding and modification of histones from reference cell types when chromatin profiles are similar to the query. Epitome achieves state-of-the-art accuracy when predicting transcription factor binding sites on novel cellular contexts and can further improve predictions as more epigenetic signals are collected from both reference cell types and the query cellular context of interest.

Automated summary

Epitome is a deep neural network that achieves state-of-the-art accuracy in predicting transcription factor binding sites by learning chromatin accessibility similarities between reference cell types and a query cellular context, and copying over signals from reference cell types when chromatin profiles are similar. It can further improve predictions as more epigenetic signals are collected.