DeepMILO: A deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
DeepMILO: A deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on variants from whole-genome sequences of 1834 patients of twelve cancer types revealed 672 insulator loops disrupted in at least 10% of patients.