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DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
2020
Genome Biology
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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. Our results show mutations at loop anchors are associated with upregulation of the cancer driver genes
doi:10.1186/s13059-020-01987-4
pmid:32216817
pmcid:PMC7098089
fatcat:nzx6hg6cybabxhcezfoadckyuu