Genomic language model mitigates chimera artifacts in nanopore direct RNA sequencing

1. Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201–206 (2018).

   Google Scholar 

2. Jain, M., Abu-Shumays, R., Olsen, H. E. & Akeson, M. Advances in Nanopore Direct RNA Sequencing. Nat. Methods 19, 1160–1164 (2022).

   Google Scholar 

3. Zou, Y. et al. A Comparative Evaluation of Computational Models for RNA Modification Detection Using Nanopore Sequencing with RNA004 Chemistry. Brief. Bioinforma. 26, bbaf404 (2025).

   Google Scholar 

4. Martin, A. et al. Molecular Barcoding of Native RNAs Using Nanopore Sequencing and Deep Learning. Genome Res. 30, 1345–1353 (2020).

   Google Scholar 

5. GitHub – nanoporetech/dorado: Oxford Nanopore’s Basecaller. https://github.com/nanoporetech/dorado (2023).

6. Liu-Wei, W. et al. Sequencing accuracy and systematic errors of nanopore direct RNA sequencing. BMC Genomics 25, 528 (2024).

   Google Scholar 

7. GitHub – epi2me-labs/pychopper: cDNA read preprocessing. https://github.com/epi2me-labs/pychopper (2024).

8. Wick, R.R., Judd, L.M., Gorrie, C.L. and Holt, K.E. Completing bacterial genome assemblies with multiplex MinION sequencing. Microb. Genom. 3, e000132 (2017).

9. Bonenfant, Q., Noé, L. & Touzet, H. élène Porechop_ABI: Discovering Unknown Adapters in Oxford Nanopore Technology Sequencing Reads for Downstream Trimming. Bioinforma. Adv. 3, vbac085 (2023).

   Google Scholar 

10. Benegas, G., Ye, C., Albors, C., Li, J.C. and Song, Y.S. Genomic language models: opportunities and challenges. Trends in Genetics (2025).

11. Nguyen, E. et al. Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution. Adv. Neural Inform. Proces. Sys. 36, 43177–43201 (2023).

12. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. of the IEEE conference on computer vision and pattern recognition pages 770–778 (2016).

13. Ji, Y., Zhou, Z., Liu, H. & Davuluri, R. V. DNABERT: Pre-Trained Bidirectional Encoder Representations from Transformers Model for DNA-language in Genome. Bioinformatics 37, 2112–2120 (2021).

    Google Scholar 

14. Zhou, Z. et al. Dnabert-2: Efficient foundation model and benchmark for multi-species genome. arXiv, (2023).

15. Dalla-Torre, H. et al. Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics. Nat. Methods 22, 287–297 (2025).

16. Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386, eado9336 (2024).

    Google Scholar 

17. Chen, Y. et al. A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines. Nat. Methods 1–12 (2025).

18. Heinz, J. M., Meyerson, M. & Li, H. Detecting Foldback Artifacts in Long-Reads. bioRxiv pp 2025-07 (2025).

19. Oxford Nanopore PLC. Chemistry Technical Document (CHTD_500_v1_revAQ_ 07Jul2016) (2017).

20. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    Google Scholar 

21. Kent, W. J. BLAT—the BLAST-like Alignment Tool. Genome Res. 12, 656–664 (2002).

    Google Scholar 

22. Ma, C., Shao, M. & Kingsford, C. SQUID: Transcriptomic Structural Variation Detection from RNA-seq. Genome Biology 19: 52 April (2018).

23. Pardo-Palacios, F. J. et al. Systematic assessment of long-read RNA-seq methods for transcript identification and quantification. Nat. Methods 21, 1349–1363 (2024).

24. Hewel, C. et al. Direct RNA sequencing enables improved transcriptome assessment and tracking of RNA modifications for medical applications. Nucleic Acids Res. 53, gkaf1314 (2025).

25. Prjibelski, A. D. et al. Accurate isoform discovery with IsoQuant using long reads. Nat. Biotechnol. 41, 915–918 (2023).

    Google Scholar 

26. Chen, Y. et al. Gene fusion detection and characterization in long-read cancer transcriptome sequencing data with FusionSeeker. Cancer Res. 83, 28–33 (2023).

    Google Scholar 

27. Uhrig, S. et al. Accurate and Efficient Detection of Gene Fusions from RNA Sequencing Data. Genome Res. 31, 448–460 (2021).

    Google Scholar 

28. Schulz, L. et al. Direct Long-Read RNA Sequencing Identifies a Subset of Questionable Exitrons Likely Arising from Reverse Transcription Artifacts. Genome Biol. 22, 190 (2021).

    Google Scholar 

29. Balázs, Z. et al. Template-Switching Artifacts Resemble Alternative Polyadenylation. BMC Genomics 20, 824 (2019).

    Google Scholar 

30. Sessegolo, C. et al. Transcriptome Profiling of Mouse Samples Using Nanopore Sequencing of cDNA and RNA Molecules. Sci. Rep. 9, 14908 (2019).

    Google Scholar 

31. Felton, C., Tang, A.D., Knisbacher, B.A., Wu, C.J. & Brooks, A.N. Detection of alternative isoforms of gene fusions from long-read RNA-seq with FLAIR-fusion. bioRxiv, pages 2022–08, (2022).

32. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Google Scholar 

33. Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR), pages 1–15 (2015).

34. Paszke, A. et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32, (2019).

35. GitHub – facebookresearch/hydra: A framework for elegantly configuring complex applications. https://github.com/facebookresearch/hydra, (2024).

36. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).

    Google Scholar 

37. Grünberger, F., Ferreira-Cerca, S. ébastien & Grohmann, D. Nanopore Sequencing of RNA and cDNA Molecules in Escherichia Coli. RNA 28, 400–417 (2022).

    Google Scholar 

38. Li, Y. & Yang, R. PxBLAT: An Efficient Python Binding Library for BLAT. BMC Bioinforma. 25, 219 (2024).

    Google Scholar 

39. Tardaguila, M. et al. SQANTI: Extensive Characterization of Long-Read Transcript Sequences for Quality Control in Full-Length Transcriptome Identification and Quantification. Genome Res. 28, 396–411 (2018).

    Google Scholar 

40. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Google Scholar 

41. Dobin, A. et al. STAR: Ultrafast Universal RNA-seq Aligner. Bioinformatics 29, 15–21 (2013).

    Google Scholar 

42. Hu, B. et al. GSDS 2.0: An Upgraded Gene Feature Visualization Server. Bioinformatics 31, 1296–1297 (2015).

    Google Scholar 

43. Samarakoon, H. et al. Interactive Visualization of Nanopore Sequencing Signal Data with Squigualiser. Bioinformatics 40, btae501 (2024).

    Google Scholar 

44. Lågstad, S. et al. Chimeraviz: A Tool for Visualizing Chimeric RNA. Bioinformatics 33, 2954–2956 (2017).

    Google Scholar 

45. Sherman, B. T. et al. DAVID: A Web Server for Functional Enrichment Analysis and Functional Annotation of Gene Lists (2021 Update). Nucleic Acids Res. 50, W216–W221 (2022).

    Google Scholar 

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Author notes

1. These authors contributed equally: Yangyang Li, Ting-You Wang.

Authors and Affiliations

1. Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

   Yangyang Li, Ting-You Wang, Qingxiang Guo, Yanan Ren, Xiaotong Lu, Qi Cao & Rendong Yang

2. Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

   Qi Cao & Rendong Yang

Contributions

Y.L., T.Y.W. and R.Y. designed the study with Q.C. Y.L. and T.Y.W. performed the analysis. Q.G., Y.R. and X.L. performed the experiments. YL designed and implemented the model and computational tool. Y.L., T.Y.W., Q.G. and R.Y. wrote the manuscript. R.Y. supervised this work.

Corresponding author

Correspondence to Rendong Yang.

Competing interests

R.Y. has served as an advisor/consultant for Tempus AI, Inc. This relationship is unrelated to and did not influence the research presented in this study. Other authors have no competing interests.

Peer review information

Nature Communications thanks Ying Chen, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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