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• W3130800274 abstract "N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA’s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana , Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression." @default.
• W3130800274 created "2021-03-01" @default.
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• W3130800274 date "2021-02-18" @default.
• W3130800274 modified "2023-10-15" @default.
• W3130800274 title "Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species" @default.
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• W3130800274 doi "https://doi.org/10.1371/journal.pcbi.1008767" @default.
• W3130800274 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7924747" @default.
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• W3130800274 hasPublicationYear "2021" @default.
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