Related Experiment Video
Updated: Jan 19, 2026
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes
Song-Yao Zhang1, Shao-Wu Zhang1, Xiao-Nan Fan1
1Key Laboratory of Information Fusion Technology of Ministry of Education, Department of intelligent science and technology, School of Automation, Northwestern Polytechnical University, Xían, China.
Motivation:
As 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.
Results:
To 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.
Insights
This study introduces FunDMDeep-m6A, a new pipeline to identify context-specific N6-methyladenosine (m6A) functional genes involved in stem cell differentiation and cancer. It enhances understanding of m6A
Area of Science:
- Epigenetics and RNA Biology
- Computational Biology and Bioinformatics
- Genomics and Molecular Biology
Background:
- N6-methyladenosine (m6A) is the most abundant mRNA modification in mammals, crucial for mRNA metabolism, stem cell differentiation, and cancer.
- Dynamic changes in m6A levels and their specific gene functions in biological processes remain largely unknown.
Purpose of the Study:
- To develop FunDMDeep-m6A, a novel pipeline for identifying context-specific m6A-mediated functional genes.
- To address the elusiveness surrounding dynamic m6A changes and their roles in stem cell differentiation and cancer.
Main Methods:
- Developed DMDeep-m6A, a deep learning model and statistical test for single-base resolution differential m6A methylation (DmM) site identification from MeRIP-Seq data.
- Integrated DmM genes with differential expression analysis using a network-based method, including a novel m6A-signaling bridge (MSB) score.
- Utilized a heat diffusion process in protein-protein interaction (PPI) networks to quantify the functional significance of DmM genes.
Main Results:
- FunDMDeep-m6A identified more context-specific and functionally significant differential m6A methylation genes (FDmMGenes) compared to m6A-Driver.
- Functional enrichment analysis revealed m6A targets key genes in embryonic development, stem cell differentiation, transcription, translation, cell death, proliferation, and cancer pathways.
- Demonstrated the pipeline's power in elucidating m6A regulatory functions in biological processes and diseases.
Conclusions:
- FunDMDeep-m6A effectively identifies context-specific m6A-mediated functional genes.
- The findings highlight m6A's critical roles in fundamental biological processes and disease pathogenesis.
- The pipeline provides a powerful tool for advancing m6A-related research.
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