ArticlePLoS computational biology2021
Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
- Deep learning-based identification of N6-methyladenine sites via hybrid feature fusion and SHAP-driven feature selection.Scientific reports · 2026Article
- Mamba6mA: a Mamba-based DNA N6-methyladenine site prediction model.Bioinformatics (Oxford, England) · 2026Article
- Designing RNA sequencing experiments: A practical guide to reproducible gene expression analysis.Computational and structural biotechnology journal · 2026Review
- DNA Methylation Recognition Using Hybrid Deep Learning with Dual Nucleotide Visualization Fusion Feature Encoding.Interdisciplinary sciences, computational life sciences · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- N6-methyladenine identification using deep learning and discriminative feature integration.BMC medical genomics · 2025Article
- Methyl-GP: accurate generic DNA methylation prediction based on a language model and representation learning.Nucleic acids research · 2025Article
- Identification of DNA N6-methyladenine modifications in the rice genome with a fine-tuned large language model.Frontiers in plant science · 2025Article
- FSFT6mA: a feature-synthesis fine-tuning framework for DNA 6mA site prediction.Frontiers in genetics · 2025Article
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- Deep learning modeling of RNA ac4C deposition reveals the importance of plant alternative splicing.Plant molecular biology · 2024Article
- Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.BMC genomics · 2023Article
- StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning.BMC genomics · 2023Article
- 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site.BioData mining · 2023Article
- Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.PLoS computational biology · 2023Article
- Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants.Plant communications · 2023Review
- CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction.Computational and structural biotechnology journal · 2023Article
- SARS-CoV-2 virus classification based on stacked sparse autoencoder.Computational and structural biotechnology journal · 2023Article
- MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction.GigaScience · 2022Article
- iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations.Genome biology · 2022Article
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8 authors.
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No grant is acknowledged in the PubMed record.
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.
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