In one paragraphArticle in Nucleic acids research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
2 · The registryThe 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
12 authors.
Haozhe WangDepartment of Biosciences and Bioinformatics, Center for Intelligent RNA Therapeutics, Suzhou Key Laboratory of Cancer Biology and Chronic Disease, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.ORCID 0009-0002-1713-0760 Bowen SongDepartment of Public Health, School of Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China.ORCID 0000-0002-8586-0573 Zhixing WuDepartment of Biosciences and Bioinformatics, Center for Intelligent RNA Therapeutics, Suzhou Key Laboratory of Cancer Biology and Chronic Disease, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.
Yigan ZhangInstitute of Biomedical Research, Regulatory Mechanism and Targeted Therapy for Liver Cancer Shiyan Key Laboratory, Hubei provincial Clinical Research Center for Precise Diagnosis and Treatment of Liver Cancer, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, China.
Jiayi LiDepartment of Biosciences and Bioinformatics, Center for Intelligent RNA Therapeutics, Suzhou Key Laboratory of Cancer Biology and Chronic Disease, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.
Yuxin ZhangState Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0003-1900-6712 Anh NguyenDepartment of Computer Science, University of Liverpool, Liverpool L7 8TX, United Kingdom.
Jionglong SuSchool of AI and Advanced Computing, XJTLU Entrepreneur College, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.ORCID 0000-0001-5360-6493 Daniel J RigdenInstitute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L7 8TX, United Kingdom.ORCID 0000-0002-7565-8937 Yijun TangInstitute of Biomedical Research, Regulatory Mechanism and Targeted Therapy for Liver Cancer Shiyan Key Laboratory, Hubei provincial Clinical Research Center for Precise Diagnosis and Treatment of Liver Cancer, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, China.
Guifang JiaSynthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.ORCID 0000-0002-4186-6922 Jia MengDepartment of Biosciences and Bioinformatics, Center for Intelligent RNA Therapeutics, Suzhou Key Laboratory of Cancer Biology and Chronic Disease, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.ORCID 0000-0003-3455-205X Funding
Supercomputing Platform of Xi'an Jiaotong-Liverpool University
6 · The paper itselfAbstract
RNA modifications critically regulate RNA function and fate, yet their dynamic changes across the RNA life cycle and during cellular transitions remain largely unexplored. Here we introduce VeloRM, a computational framework that captures RNA modification dynamics at single-cell resolution. VeloRM uniquely disentangles presplicing and postsplicing epitranscriptomes, enabling for the first time the identification and differentiation of modification sites on transcripts before versus after splicing. By modeling the velocity of epitranscriptomic changes, VeloRM predicts future RNA modification states and reconstructs cellular trajectories directly from epitranscriptome information. Application to single-cell datasets profiling m6A and A-to-I editing demonstrates that VeloRM accurately recapitulates known trajectory patterns in cell cycle and differentiation. Notably, VeloRM reveals for the first time a set of m6A sites that are hyper-methylated on prespliced RNAs near splice junctions, with dynamic patterns that unveil clear functional implications in splicing regulation. VeloRM represents a rigorous yet powerful tool that opens unprecedented opportunities to study epitranscriptome dynamics during biological transitions.
Indexed as
RNARNA Processing, Post-TranscriptionalRNA SplicingSingle-Cell AnalysisAdenosineEpitranscriptomeEpitranscriptomicsHumansRNA MethylationSingle-Cell Gene Expression AnalysisAdenosineRNA
Identifiers
PMID42376818
PMCPMC13316427
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