Zhou, Jingxian, Wang, Xuan, Wei, Zhen, Meng, Jia 
ORCID: 0000-0003-3455-205X and Huang, Daiyun
(2022)
4acCPred: Weakly supervised prediction of Lambda l4-acetyldeoxycytosine DNA modification from sequences.
MOLECULAR THERAPY-NUCLEIC ACIDS, 30.
pp. 337-345.
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4acCPred Weakly supervised prediction of iNi sup4sup-acetyldeoxycytosine DNA modification from sequences.pdf - Published version Download (1MB) | Preview |
Abstract
DNA methylation is one of the earliest epigenetic regulation mechanisms studied extensively, and it is critical for normal development, diseases, and gene expression. As a recently identified chemical modification of DNA, N4-acetyldeoxycytosine (4acC) was shown to be abundant in <i>Arabidopsis</i> and highly associated with gene expression and actively transcribed genes. Precise identification of 4acC is essential for studying its biological function. We proposed the 4acCPred, the first computational framework for predicting 4acC-carrying regions from <i>Arabidopsis</i> genomic DNA sequences. Since the existing 4acC data are not precise for a specific base but only report regions that are hundreds of bases long, we formulated the task as a weakly supervised learning problem and built 4acCPred using a multi-instance-based deep neural network. Both cross-validation and independent testing on the four datasets under different conditions show promising performance, with mean areas under the receiver operating characteristic curve (AUCs) of 0.9877 and 0.9899, respectively. 4acCPred also provides motif mining through model interpretation. The motifs found by 4acCPred are consistent with existing knowledge, indicating that the model successfully captured real biological signals. In addition, a user-friendly web server was built to facilitate 4acC prediction, motif visualization, and data access. Our framework and web server should serve as useful tools for 4acC research.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | DNA modification, MT: Bioinformatics, N4-acetyldeoxycytosine, deep neural network, multiple-instance learning, sequence motif, weakly supervised learning |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 23 Dec 2022 14:26 |
| Last Modified: | 23 Dec 2022 14:26 |
| DOI: | 10.1016/j.omtn.2022.10.004 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3166783 |
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