ArticlePLoS computational biology2016
m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 47 citations in OpenAlex.
- mInterdisciplinary sciences, computational life sciences · 2026Article
- A novel biclustering algorithm for mining m6A co-methylation patterns based on beta-binomial distribution and data screening strategy.PLoS computational biology · 2026Article
- Deep learning model and omics screening highlight angiotensinogen as a 5-methylcytosine (mFrontiers in immunology · 2026Article
- Block sparse Bayes-based fuzzy system for RNA N6-methyladenosine sites prediction.PLoS computational biology · 2025Article
- Epitranscriptomic sculpting: the role of mMedical oncology (Northwood, London, England) · 2025Review
- RBM15 promotes hypoxia/reoxygenation-induced ferroptosis in human cardiomyocytes by mediating m6A modification of ACSL4.Hereditas · 2025Article
- Writers, readers, and erasers RNA modifications and drug resistance in cancer.Molecular cancer · 2024Review
- epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation.Briefings in bioinformatics · 2024Article
- Bioinformatics identification and experimental validation of m6A-related diagnostic biomarkers in the subtype classification of blood monocytes from postmenopausal osteoporosis patients.Frontiers in endocrinology · 2023Article
- Analysis of N6-Methyladenosine RNA Methylation Regulators in Diagnosis and Distinct Molecular Subtypes of Ankylosing Spondylitis.Disease markers · 2022Article
- Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.Frontiers in oncology · 2022Review
- m6A-express: uncovering complex and condition-specific m6A regulation of gene expression.Nucleic acids research · 2021Article
- Significance of RNA N6-Methyladenosine Regulators in the Diagnosis and Subtype Classification of Childhood Asthma Using the Gene Expression Omnibus Database.Frontiers in genetics · 2021Article
- Wilms' Tumor 1-Associated Protein Contributes to Chemo-Resistance to Cisplatin Through the Wnt/β-Catenin Pathway in Endometrial Cancer.Frontiers in oncology · 2021Article
- Recent advances in functional annotation and prediction of the epitranscriptome.Computational and structural biotechnology journal · 2021Review
- Progression of Thyroid Carcinoma Is Promoted by the m6A Methyltransferase METTL3 Through Regulating mOncoTargets and therapy · 2020Article
- The dynamics of FTO binding and demethylation from the mRNA biology · 2019Article
- FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes.Bioinformatics (Oxford, England) · 2019Article
- Functions of RNA N6-methyladenosine modification in cancer progression.Molecular biology reports · 2019Review
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.