ArticleNucleic acids research2024
m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.Briefings in bioinformatics · 2026Review
- m6AConquer: a consistently quantified and orthogonally validated database for the N6-methyladenosine (m6A) epitranscriptome.Nucleic acids research · 2026Article
- Multi-region mNature neuroscience · 2026Article
- Machine learning-augmented m6A-Seq analysis without a reference genome.Briefings in bioinformatics · 2025Article
- Exploring m6A modifications in gastric cancer: from molecular mechanisms to clinical applications.European journal of medical research · 2025Review
- Review
- Statistical modeling of immunoprecipitation efficiency of MeRIP-seq data enabled accurate detection and quantification of epitranscriptome.Computational and structural biotechnology journal · 2025Article
- The epitranscriptome meets non-coding RNA: m6A-mediated regulation in oncogenesis and therapy.Frontiers in cell and developmental biology · 2025Review
- N6-methyladenosine RNA modification in head and neck squamous cell carcinoma (HNSCC): current status and future insights.Medical oncology (Northwood, London, England) · 2024Review
- A Compendium of G-Flipon Biological Functions That Have Experimental Validation.International journal of molecular sciences · 2024Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
Abstract
We present m6ACali, a novel machine-learning framework aimed at enhancing the accuracy of N6-methyladenosine (m6A) epitranscriptome profiling by reducing the impact of non-specific antibody enrichment in MeRIP-Seq. The calibration model serves as a genomic feature-based classifier that refines the identification of m6A sites, distinguishing those genuinely present from those that can be detected in in-vitro transcribed (IVT) control experiments. We find that m6ACali effectively identifies non-specific binding peaks reported by exomePeak2 and MACS2 in novel MeRIP-Seq datasets without the need for paired IVT controls. The model interpretation revealed that off-target antibody binding sites commonly occur at short exons and short mRNAs, originating from high read coverage regions that share the motif sequence with true m6A sites. We also reveal that the ML strategy can efficiently adjust differentially methylated peaks and other antibody-dependent, base-resolution m6A detection techniques. As a result, m6ACali offers a promising method for the universal enhancement of m6A profiles generated by MeRIP-Seq experiments, elevating the benchmark for omics-level m6A data integration.
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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 Socratic graph.