DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure. Academic Article uri icon

Overview

abstract

  • 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 BCL2 and MYC in malignant lymphoma thus pointing to a possible new mechanism for their dysregulation via alteration of insulator loops.

publication date

  • March 26, 2020

Research

keywords

  • Chromatin
  • Deep Learning
  • Insulator Elements
  • Neoplasms

Identity

PubMed Central ID

  • PMC7098089

Scopus Document Identifier

  • 85082532375

Digital Object Identifier (DOI)

  • 10.1186/s13059-020-01987-4

PubMed ID

  • 32216817

Additional Document Info

volume

  • 21

issue

  • 1