ArticleBioinformatics (Oxford, England)2022
Differential RNA methylation analysis for MeRIP-seq data under general experimental design.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- cyberDMR: accurate and robust identification of differentially methylated regions from WGS-derived methylomes.BMC biology · 2026Article
- Flipper: An advanced framework for identifying differential RNA binding behavior with eCLIP data.bioRxiv : the preprint server for biology · 2026Article
- A comprehensive review of m6 A methylation in coronary heart disease.Journal of molecular medicine (Berlin, Germany) · 2025Review
- Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.Cell genomics · 2025Article
- M6Allele: a toolkit for detection of allele-specific RNA N6-methyladenosine modifications.GigaScience · 2025Article
- Statistical modeling of immunoprecipitation efficiency of MeRIP-seq data enabled accurate detection and quantification of epitranscriptome.Computational and structural biotechnology journal · 2025Article
- Altered m6A RNA methylation profiles in depression implicate the dysregulation of discrete cellular functions in males and females.iScience · 2024Article
- ALKBH5 regulates chicken adipogenesis by mediating LCAT mRNA stability depending on mBMC genomics · 2024Article
- magpie: A power evaluation method for differential RNA methylation analysis in N6-methyladenosine sequencing.PLoS computational biology · 2024Article
- m6A-Atlas v2.0: updated resources for unraveling the N6-methyladenosine (m6A) epitranscriptome among multiple species.Nucleic acids research · 2024Article
- Evaluation of epitranscriptome-wide N6-methyladenosine differential analysis methods.Briefings in bioinformatics · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
motivationRNA epigenetics is an emerging field to study the post-transcriptional gene regulation. The dynamics of RNA epigenetic modification have been reported to associate with many human diseases. Recently developed high-throughput technology named Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) enables the transcriptome-wide profiling of N6-methyladenosine (m6A) modification and comparison of RNA epigenetic modifications. There are a few computational methods for the comparison of mRNA modifications under different conditions but they all suffer from serious limitations.
resultsIn this work, we develop a novel statistical method to detect differentially methylated mRNA regions from MeRIP-seq data. We model the sequence count data by a hierarchical negative binomial model that accounts for various sources of variations and derive parameter estimation and statistical testing procedures for flexible statistical inferences under general experimental designs. Extensive benchmark evaluations in simulation and real data analyses demonstrate that our method is more accurate, robust and flexible compared to existing methods. AVAILABILITY AND IMPLEMENTATION: Our method TRESS is implemented as an R/Bioconductor package and is available at https://bioconductor.org/packages/devel/TRESS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
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.