ArticleNucleic acids research2021
Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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Who cites it
76 citing papers in PubMed, 113 citations in OpenAlex.
- Nonenzymatic detection and imaging of m6A-methylated RNA via proximity hybridization-initiated hybridization chain reaction with site-specific recognition.Analytical and bioanalytical chemistry · 2026Article
- Direct and indirect regulation of fetal globin transcript by the RNA-binding protein IGF2BP1.Blood · 2026Article
- Toward A Pre-disease State-centered New Paradigm in Multi-omics Research.Genomics, proteomics & bioinformatics · 2026Article
- RNA Modifications as Molecular Regulators of Alveolar Epithelial Injury and Aberrant Repair in Pulmonary Fibrosis.Biomolecules · 2026Review
- Trdn-as directs m6A-dependent transcriptional termination for accurate triadin isoform switching, preventing aberrant dyads and cardiomyopathy.Nature communications · 2026Article
- A novel method for the identification and quantification of N6-methyladenosine motifs in RNA transcripts.Molecular biology reports · 2026Article
- Epigenetic research methods and animal models for intervertebral disc degeneration (Review).Molecular medicine reports · 2026Review
- mArXiv · 2026Article
- RBM45 Preferential Binding to mThe journal of physical chemistry. B · 2026Article
- The impact of m6A modified cargo conveyed by exosomes on the tumor microenvironment.Discover oncology · 2026Review
- Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.Briefings in bioinformatics · 2026Review
- RNA N6-methyladenosine (m6A) regulates cell cycle progression in diffuse midline glioma (DMG) and confers sensitivity to FTO inhibition.Cell death & disease · 2026Article
- Systematic identification of tissue-conserved mbioRxiv : the preprint server for biology · 2026Article
- Raw signal segmentation for estimating RNA modification from Nanopore direct RNA sequencing data.eLife · 2026Article
- Targeting intracellular mRNA mJournal of advanced research · 2026Review
- Review
- Epitranscriptomic modifications in embryonic development: insights into natural and ART-induced mechanisms and implications.Human reproduction update · 2026Review
- Domain-derived knowledge enabled machine learning and functional characterization of cancer-associated RNA methylation sites.BMC bioinformatics · 2026Article
- RMzyme: regulations of RNA-modifying enzymes in humans.Signal transduction and targeted therapy · 2026Article
- Meta-unstable mRNAs in activated CD8Nature communications · 2026Article
16 more citing papers are in PubMed but not listed here.
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Authors and funding
18 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based approach to map m6A sites with single-nucleotide resolution. However, due to broad antibody reactivity, reliable identification of m6A sites from miCLIP data remains challenging. Here, we present miCLIP2 in combination with machine learning to significantly improve m6A detection. The optimized miCLIP2 results in high-complexity libraries from less input material. Importantly, we established a robust computational pipeline to tackle the inherent issue of false positives in antibody-based m6A detection. The analyses were calibrated with Mettl3 knockout cells to learn the characteristics of m6A deposition, including m6A sites outside of DRACH motifs. To make our results universally applicable, we trained a machine learning model, m6Aboost, based on the experimental and RNA sequence features. Importantly, m6Aboost allows prediction of genuine m6A sites in miCLIP2 data without filtering for DRACH motifs or the need for Mettl3 depletion. Using m6Aboost, we identify thousands of high-confidence m6A sites in different murine and human cell lines, which provide a rich resource for future analysis. Collectively, our combined experimental and computational methodology greatly improves m6A identification.
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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.