DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure.
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ABSTRACT:
SUBMITTER: Trieu T
PROVIDER: S-EPMC7098089 | biostudies-literature | 2020 Mar
REPOSITORIES: biostudies-literature
Publications
DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure.
Trieu Tuan T Martinez-Fundichely Alexander A Khurana Ekta E
Genome biology 20200326 1
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 BCL ...[more]
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