Evidence map›Paper›PMID 41145497›Full record

ArticleNature communications2025

DirectRM: integrated detection of landscape and crosstalk between multiple RNA modifications using direct RNA sequencing.

Yuxin Zhang, Yuecheng Wu, Jiongming Ma, Yiyu Wu, Liying Li, Haozhe Wang, Guifang Jia, Daniel J Rigden, Jia Meng, Daiyun Huang and 1 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What 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 registry

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.

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 literature

Who cites it

19 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Emerging roles of non-mNature plants · 2026
    Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. The changing landscape of gene expression analysis.Briefings in bioinformatics · 2026
    Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yuxin Zhang *Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, China.
Yuecheng Wu *Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, China.
Jiongming MaSuzhou Key Laboratory of Cancer Biology and Chronic Diseases, Department of Biosciences and Bioinformatics, Center for Intelligent RNA Therapeutics, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, China.
Yiyu WuKey Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, China.
Liying LiKey Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, China.
Haozhe WangSuzhou Key Laboratory of Cancer Biology and Chronic Diseases, Department of Biosciences and Bioinformatics, Center for Intelligent RNA Therapeutics, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, China.ORCID http://orcid.org/0009-0002-1713-0760
Guifang JiaBeijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of the Ministry of Education, Synthetic and Functional Biomolecules Center, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.ORCID http://orcid.org/0000-0002-4186-6922
Daniel J RigdenInstitute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
Jia MengSuzhou Key Laboratory of Cancer Biology and Chronic Diseases, Department of Biosciences and Bioinformatics, Center for Intelligent RNA Therapeutics, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, China. jia.meng@xjtlu.edu.cn.ORCID http://orcid.org/0000-0003-3455-205X
Daiyun HuangWisdom Lake Academy of Pharmacy, Center for Intelligent RNA Therapeutics, Xi'an Jiaotong-Liverpool University, Suzhou, China. daiyun.huang02@xjtlu.edu.cn.ORCID http://orcid.org/0000-0002-3067-7165
Kunqi ChenKey Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, Fuzhou, China. kunqi.chen@fjmu.edu.cn.ORCID http://orcid.org/0000-0002-6025-8957

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Profiling RNA modifications is essential to understand their functions and interactions. By taking the advantages of nanopore direct RNA sequencing, we present DirectRM, enabling simultaneous detection of six abundant modifications (N4-acetylcytidine, 1-methyladenosine, 5-methylcytidine, N7-methlguanosine, N6-methyladenosine, and pseudouridine) in native RNAs. Its two-stage pipeline identifies candidate modified kmers using binary classifier, then determines specific modifications and positions using an attention-based neural network. Trained with molecule-level features extracted from native RNA samples and validated on human cell lines and viral RNAs, DirectRM demonstrates high sensitivity, precision and robustness, outperforming existing tools. Crucially, we reveal the associations between modifications at both transcript and molecule-level. Modifications tend to proximate to each other on the transcript level, while at the molecule level, the presence of one modification is likely to reduce the occurrence of modifications at adjacent positions. DirectRM offers a powerful approach for studying epitranscriptome complexity and is expandable for future research.

Indexed as

RNARNA Processing, Post-TranscriptionalSequence Analysis, RNA5-MethylcytosineAdenosineCytidineHumansNeural Networks, ComputerRNA, ViralTranscriptome1-methyladenosine5-methylcytidine5-MethylcytosineAdenosineCytidineN-methyladenosineRNARNA, Viral

Identifiers

PMID41145497
PMCPMC12559715

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.