Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization
© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. Despite large experimental and computational efforts aiming to dissect the mechanisms underlying disease risk, mapping cis-regulatory elements to target genes remains a challenge. Here, we introduce a mat...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2021
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| Online Access: | https://hdl.handle.net/1721.1/134786 |
| _version_ | 1811072998304645120 |
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| author | Liu, Dianbo Davila-Velderrain, Jose Zhang, Zhizhuo Kellis, Manolis |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Liu, Dianbo Davila-Velderrain, Jose Zhang, Zhizhuo Kellis, Manolis |
| author_sort | Liu, Dianbo |
| collection | MIT |
| description | © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. Despite large experimental and computational efforts aiming to dissect the mechanisms underlying disease risk, mapping cis-regulatory elements to target genes remains a challenge. Here, we introduce a matrix factorization framework to integrate physical and functional interaction data of genomic segments. The framework was used to predict a regulatory network of chromatin interaction edges linking more than 20 000 promoters and 1.8 million enhancers across 127 human reference epigenomes, including edges that are present in any of the input datasets. Our network integrates functional evidence of correlated activity patterns from epigenomic data and physical evidence of chromatin interactions. An important contribution of this work is the representation of heterogeneous data with different qualities as networks. We show that the unbiased integration of independent data sources suggestive of regulatory interactions produces meaningful associations supported by existing functional and physical evidence, correlating with expected independent biological features. |
| first_indexed | 2024-09-23T09:27:02Z |
| format | Article |
| id | mit-1721.1/134786 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:27:02Z |
| publishDate | 2021 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1347862023-02-23T14:58:55Z Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization Liu, Dianbo Davila-Velderrain, Jose Zhang, Zhizhuo Kellis, Manolis Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. Despite large experimental and computational efforts aiming to dissect the mechanisms underlying disease risk, mapping cis-regulatory elements to target genes remains a challenge. Here, we introduce a matrix factorization framework to integrate physical and functional interaction data of genomic segments. The framework was used to predict a regulatory network of chromatin interaction edges linking more than 20 000 promoters and 1.8 million enhancers across 127 human reference epigenomes, including edges that are present in any of the input datasets. Our network integrates functional evidence of correlated activity patterns from epigenomic data and physical evidence of chromatin interactions. An important contribution of this work is the representation of heterogeneous data with different qualities as networks. We show that the unbiased integration of independent data sources suggestive of regulatory interactions produces meaningful associations supported by existing functional and physical evidence, correlating with expected independent biological features. 2021-10-27T20:09:10Z 2021-10-27T20:09:10Z 2019 2021-01-05T18:25:15Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134786 en 10.1093/NAR/GKZ538 Nucleic Acids Research Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Oxford University Press (OUP) Oxford University Press |
| spellingShingle | Liu, Dianbo Davila-Velderrain, Jose Zhang, Zhizhuo Kellis, Manolis Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization |
| title | Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization |
| title_full | Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization |
| title_fullStr | Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization |
| title_full_unstemmed | Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization |
| title_short | Integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization |
| title_sort | integrative construction of regulatory region networks in 127 human reference epigenomes by matrix factorization |
| url | https://hdl.handle.net/1721.1/134786 |
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