ArticleBioinformatics (Oxford, England)2023
Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 19 citations in OpenAlex.
- A novel method for the identification and quantification of N6-methyladenosine motifs in RNA transcripts.Molecular biology reports · 2026Article
- Sequence determinant and functional relevance of 8-oxoguanine RNA modification unveiled from foundation-model-based predictor.Molecular therapy. Nucleic acids · 2026Article
- Integrative interpretable learning reveals shared patterns of epitranscriptomic regulation across multiple cancer types.BMC biology · 2026Article
- EvoRMD: integrating biological context and evolutionary RNA language models for interpretable prediction of RNA modifications.Genome biology · 2026Article
- Mke-resnet: a lightweight and interpretable deep learning framework for efficient RNA m6A site identification.BMC bioinformatics · 2026Article
- Epitranscriptomic modifications in embryonic development: insights into natural and ART-induced mechanisms and implications.Human reproduction update · 2026Review
- OMetaNet: an efficient hybrid deep learning model based on multimodal data fusion and contrastive learning for predicting 2'-O-methylation sites in human RNA.BMC bioinformatics · 2025Article
- Deciphering RNA modification and post-transcriptional regulation with NetRNApan.Briefings in bioinformatics · 2025Article
- Interpretability-guided RNA NCommunications biology · 2025Article
- Multimodal zero-shot learning of previously unseen epitranscriptomes from RNA-seq data.Briefings in bioinformatics · 2025Article
- Methyl-GP: accurate generic DNA methylation prediction based on a language model and representation learning.Nucleic acids research · 2025Article
- Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.Cell genomics · 2025Article
- Interpretable deep cross networks unveiled common signatures of dysregulated epitranscriptomes across 12 cancer types.Molecular therapy. Nucleic acids · 2024Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
motivationRNA N6-methyladenosine (m6A) in Homo sapiens plays vital roles in a variety of biological functions. Precise identification of m6A modifications is thus essential to elucidation of their biological functions and underlying molecular-level mechanisms. Currently available high-throughput single-nucleotide-resolution m6A modification data considerably accelerated the identification of RNA modification sites through the development of data-driven computational methods. Nevertheless, existing methods have limitations in terms of the coverage of single-nucleotide-resolution cell lines and have poor capability in model interpretations, thereby having limited applicability.
resultsIn this study, we present CLSM6A, comprising a set of deep learning-based models designed for predicting single-nucleotide-resolution m6A RNA modification sites across eight different cell lines and three tissues. Extensive benchmarking experiments are conducted on well-curated datasets and accordingly, CLSM6A achieves superior performance than current state-of-the-art methods. Furthermore, CLSM6A is capable of interpreting the prediction decision-making process by excavating critical motifs activated by filters and pinpointing highly concerned positions in both forward and backward propagations. CLSM6A exhibits better portability on similar cross-cell line/tissue datasets, reveals a strong association between highly activated motifs and high-impact motifs, and demonstrates complementary attributes of different interpretation strategies. AVAILABILITY AND IMPLEMENTATION: The webserver is available at http://csbio.njust.edu.cn/bioinf/clsm6a. The datasets and code are available at https://github.com/zhangying-njust/CLSM6A/.
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