DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
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 18...
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| Format: | Article |
| Language: | English |
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BMC
2020-03-01
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| Series: | Genome Biology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13059-020-01987-4 |
| _version_ | 1818293190138003456 |
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| author | Tuan Trieu Alexander Martinez-Fundichely Ekta Khurana |
| author_facet | Tuan Trieu Alexander Martinez-Fundichely Ekta Khurana |
| author_sort | Tuan Trieu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T03:11:55Z |
| format | Article |
| id | doaj.art-9f497210053349c499fa646e3899a4c7 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-12-13T03:11:55Z |
| publishDate | 2020-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-9f497210053349c499fa646e3899a4c72022-12-22T00:01:35ZengBMCGenome Biology1474-760X2020-03-0121111110.1186/s13059-020-01987-4DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structureTuan Trieu0Alexander Martinez-Fundichely1Ekta Khurana2Meyer Cancer Center, Weill Cornell MedicineMeyer Cancer Center, Weill Cornell MedicineMeyer Cancer Center, Weill Cornell MedicineAbstract 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.http://link.springer.com/article/10.1186/s13059-020-01987-43D genomeNon-coding mutationCancerBCL2MYCDeep learning |
| spellingShingle | Tuan Trieu Alexander Martinez-Fundichely Ekta Khurana DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure Genome Biology 3D genome Non-coding mutation Cancer BCL2 MYC Deep learning |
| title | DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure |
| title_full | DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure |
| title_fullStr | DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure |
| title_full_unstemmed | DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure |
| title_short | DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure |
| title_sort | deepmilo a deep learning approach to predict the impact of non coding sequence variants on 3d chromatin structure |
| topic | 3D genome Non-coding mutation Cancer BCL2 MYC Deep learning |
| url | http://link.springer.com/article/10.1186/s13059-020-01987-4 |
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