| Title | DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure. |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Trieu T, Martinez-Fundichely A, Khurana E |
| Journal | Genome Biol |
| Volume | 21 |
| Issue | 1 |
| Pagination | 79 |
| Date Published | 2020 Mar 26 |
| ISSN | 1474-760X |
| Keywords | CCCTC-Binding Factor, Cell Cycle Proteins, Cell Line, Tumor, Chromatin, Chromosomal Proteins, Non-Histone, Deep Learning, Humans, Insulator Elements, Mutation, Neoplasms, Whole Genome Sequencing |
| 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. |
| DOI | 10.1186/s13059-020-01987-4 |
| Alternate Journal | Genome Biol |
| PubMed ID | 32216817 |
| PubMed Central ID | PMC7098089 |
| Grant List | R01 CA218668 / CA / NCI NIH HHS / United States |