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.

中文翻译：

---

DeepMILO：一种预测非编码序列变异对 3D 染色质结构影响的深度学习方法

非编码变异已被证明通过 3D 基因组结构的改变与疾病相关。我们提出了一种深度学习方法 DeepMILO，以预测变体对 CTCF/cohesin 介导的绝缘体环的影响。将 DeepMILO 应用于 1834 名 12 种癌症患者的全基因组序列变体后，发现至少 10% 的患者中有 672 个绝缘体环被破坏。我们的结果表明，环锚点的突变与恶性淋巴瘤中癌症驱动基因 BCL2 和 MYC 的上调有关，因此指出了通过改变绝缘体环导致其失调的可能新机制。