ArticleBioinformatics (Oxford, England)2019
FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 38 citations in OpenAlex.
- mInterdisciplinary sciences, computational life sciences · 2026Article
- Influence of RNA Modifications on Cancer Metabolism: Interplay with Hypoxia and Therapeutic Implications.Advances in experimental medicine and biology · 2026Review
- Epigenetic regulation in female reproduction: the impact of m6A on maternal-fetal health.Cell death discovery · 2025Review
- Comprehensive analysis of mComputational and structural biotechnology journal · 2024Article
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- Recall DNA methylation levels at low coverage sites using a CNN model in WGBS.PLoS computational biology · 2023Article
- Review
- Role of m6A writers, erasers and readers in cancer.Experimental hematology & oncology · 2022Review
- Advances in the functional roles of N6-methyladenosine modification in cancer progression: mechanisms and clinical implications.Molecular biology reports · 2022Review
- Processing body (P-body) and its mediators in cancer.Molecular and cellular biochemistry · 2022Review
- A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer.Frontiers in genetics · 2022Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- m6A-express: uncovering complex and condition-specific m6A regulation of gene expression.Nucleic acids research · 2021Article
- Research progress concerning mOncology letters · 2021Review
- Article
- Expression pattern and prognostic value of N6-methyladenosine RNA methylation key regulators in hepatocellular carcinoma.Mutagenesis · 2021Article
- Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.Briefings in bioinformatics · 2021Article
- Deep learning in next-generation sequencing.Drug discovery today · 2021Review
- Recent advances in functional annotation and prediction of the epitranscriptome.Computational and structural biotechnology journal · 2021Review
- Role of m6A RNA methylation in cardiovascular disease (Review).International journal of molecular medicine · 2020Review
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
motivationAs the most abundant mammalian mRNA methylation, N6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive.
resultsTo address this, we propose in this paper FunDMDeep-m6A a novel pipeline for identifying context-specific (e.g. disease versus normal, differentiated cells versus stem cells or gene knockdown cells versus wild-type cells) m6A-mediated functional genes. FunDMDeep-m6A includes, at the first step, DMDeep-m6A a novel method based on a deep learning model and a statistical test for identifying differential m6A methylation (DmM) sites from MeRIP-Seq data at a single-base resolution. FunDMDeep-m6A then identifies and prioritizes functional DmM genes (FDmMGenes) by combing the DmM genes (DmMGenes) with differential expression analysis using a network-based method. This proposed network method includes a novel m6A-signaling bridge (MSB) score to quantify the functional significance of DmMGenes by assessing functional interaction of DmMGenes with their signaling pathways using a heat diffusion process in protein-protein interaction (PPI) networks. The test results on 4 context-specific MeRIP-Seq datasets showed that FunDMDeep-m6A can identify more context-specific and functionally significant FDmMGenes than m6A-Driver. The functional enrichment analysis of these genes revealed that m6A targets key genes of many important context-related biological processes including embryonic development, stem cell differentiation, transcription, translation, cell death, cell proliferation and cancer-related pathways. These results demonstrate the power of FunDMDeep-m6A for elucidating m6A regulatory functions and its roles in biological processes and diseases. AVAILABILITY AND IMPLEMENTATION: The R-package for DMDeep-m6A is freely available from https://github.com/NWPU-903PR/DMDeepm6A1.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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