ArticleBMC bioinformatics2017
QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
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- Short-term and long-term high-fat diet promote metabolic disorder through reprogramming mRNA mMicrobiome · 2025Article
- Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.Cell genomics · 2025Article
- Combined Transcriptomic and Epitranscriptomic Profiling Identifies THBS1 as A Regulator of Enzalutamide Resistance in Prostate Cancer.Cancer heterogeneity and plasticity · 2025Article
- Identification of key genes with abnormal RNA methylation modification and selected m6A regulators in ankylosing spondylitis.Immunity, inflammation and disease · 2024Article
- Rewiring of RNA methylation by the oncometabolite fumarate in renal cell carcinoma.NAR cancer · 2024Article
- mNAR cancer · 2023Article
- Analysis approaches for the identification and prediction ofEpigenetics · 2023Review
- Article
- Evaluation of epitranscriptome-wide N6-methyladenosine differential analysis methods.Briefings in bioinformatics · 2023Article
- Differential RNA methylation analysis for MeRIP-seq data under general experimental design.Bioinformatics (Oxford, England) · 2022Article
- m6A-express: uncovering complex and condition-specific m6A regulation of gene expression.Nucleic acids research · 2021Article
- NThe EMBO journal · 2021Article
- m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.Nucleic acids research · 2021Article
- A Novel Computational Framework to Predict Disease-Related Copy Number Variations by Integrating Multiple Data Sources.Frontiers in genetics · 2021Article
- Recent advances in functional annotation and prediction of the epitranscriptome.Computational and structural biotechnology journal · 2021Review
- Limits in the detection of mScientific reports · 2020Article
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4 authors.
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Abstract
backgroundAs a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task.
resultsWe present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes.
conclusionQNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m
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