Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet
Abstract RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-42547-1 |
| _version_ | 1797647260265218048 |
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| author | Haoran Zhu Yuning Yang Yunhe Wang Fuzhou Wang Yujian Huang Yi Chang Ka-chun Wong Xiangtao Li |
| author_facet | Haoran Zhu Yuning Yang Yunhe Wang Fuzhou Wang Yujian Huang Yi Chang Ka-chun Wong Xiangtao Li |
| author_sort | Haoran Zhu |
| collection | DOAJ |
| description | Abstract RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders. |
| first_indexed | 2024-03-11T15:13:43Z |
| format | Article |
| id | doaj.art-5ecbd87616af4963a49821f5a4f534a8 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-11T15:13:43Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-5ecbd87616af4963a49821f5a4f534a82023-10-29T12:29:33ZengNature PortfolioNature Communications2041-17232023-10-0114112210.1038/s41467-023-42547-1Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNetHaoran Zhu0Yuning Yang1Yunhe Wang2Fuzhou Wang3Yujian Huang4Yi Chang5Ka-chun Wong6Xiangtao Li7School of Artificial Intelligence, Jilin UniversityDonnelly Centre for Cellular and Biomolecular Research, University of TorontoSchool of Artificial Intelligence, Hebei University of TechnologyDepartment of Computer Science, City University of Hong KongCollege of Computer Science and Cyber Security, Chengdu University of TechnologySchool of Artificial Intelligence, Jilin UniversityDepartment of Computer Science, City University of Hong KongSchool of Artificial Intelligence, Jilin UniversityAbstract RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.https://doi.org/10.1038/s41467-023-42547-1 |
| spellingShingle | Haoran Zhu Yuning Yang Yunhe Wang Fuzhou Wang Yujian Huang Yi Chang Ka-chun Wong Xiangtao Li Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet Nature Communications |
| title | Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet |
| title_full | Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet |
| title_fullStr | Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet |
| title_full_unstemmed | Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet |
| title_short | Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet |
| title_sort | dynamic characterization and interpretation for protein rna interactions across diverse cellular conditions using hdrnet |
| url | https://doi.org/10.1038/s41467-023-42547-1 |
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