Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A deep learning method for recovering missing signals in transcriptome-wide RNA structure profiles from probing experiments

Nature Machine Intelligence volume 3, pages 995–1006 (2021)Cite this article

Subjects

Abstract

Sequencing-based RNA structure probing can generate transcriptome-wide profiles of RNA secondary structures. Sufficient structural coverage is needed to obtain unbiased insights about RNA structures and functions, yet probing methods often yield uneven coverage, with missing structural scores across many transcripts. To overcome this barrier, we developed StructureImpute, a deep learning framework inspired by depth completion from computer vision that integrates an RNA sequence with available RNA structural information of neighbouring nucleotides to infer missing structure scores. We demonstrate the strong imputation performance of StructureImpute, with accuracy much superior to predictions based on RNA sequence alone. We also show that StructureImpute reliably reconstructs RNA structural patterns at biologically impactful RNA regulation regions, including protein-binding and RNA-modification sites. Strikingly, StructureImpute can use transfer learning to apply a model trained on one dataset to accurately infer missing structural scores in other datasets, even if they were generated with different technologies (for example, icSHAPE and DMS-seq).

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The overall architecture of StructureImpute for RNA structural score imputation.
Fig. 2: Performance evaluation of StructureImpute.
Fig. 3: Gradient analysis of the contributions of RNA sequence and structural information to the imputation performance of StructureImpute.
Fig. 4: StructureImpute accurately imputes missing structural scores within functional regions.
Fig. 5: A StructureImpute model trained on one dataset accurately imputes missing structural scores in other datasets using transfer learning.
Fig. 6: Performance of StructureImpute on DMS-seq datasets.

Similar content being viewed by others

Data availability

The raw icSHAPE sequencing data were downloaded from the Gene Expression Omnibus (GEO). HEK293 whole-cell data are from GSE7435326, including both in vivo and in vitro conditions. HEK293 subcellular component (chromatin-associated, nucleoplasmic, cytoplasmic) data are from GSE117840. The m6A modification sites are from the RMBbase database46, which provides a file in .bed format with genomic coordinates of the hg38 assembly. The binding regions of the FXR2 RNA binding protein are from the CLIPDB database44, which provides a file in .bed format with hg38 assembly genomic coordinates. All the processed data are available from figshare at https://doi.org/10.6084/m9.figshare.1660685058.

Code availability

Code used for training models and performing analyses are available from GitHub (https://github.com/Tsinghua-gongjing/StructureImpute) or Zenodo (https://doi.org/10.5281/zenodo.5501018)59.

References

  1. Halvorsen, M., Martin, J. S., Broadaway, S. & Laederach, A. Disease-associated mutations that alter the RNA structural ensemble. PLoS Genet. 6, e1001074 (2010).

    Article  Google Scholar 

  2. Wapinski, O. & Chang, H. Y. Long noncoding RNAs and human disease. Trends Cell Biol. 21, 354–361 (2011).

    Article  Google Scholar 

  3. Bevilacqua, P. C., Ritchey, L. E., Su, Z. & Assmann, S. M. Genome-wide analysis of RNA secondary structure. Annu. Rev. Genet. 50, 235–266 (2016).

    Article  Google Scholar 

  4. Piao, M., Sun, L. & Zhang, Q. C. RNA regulations and functions decoded by transcriptome-wide RNA structure probing. Genomics Proteomics Bioinformatics 15, 267–278 (2017).

    Article  Google Scholar 

  5. Strobel, E. J., Yu, A. M. & Lucks, J. B. High-throughput determination of RNA structures. Nat. Rev. Genet. 19, 615–634 (2018).

    Article  Google Scholar 

  6. Rouskin, S., Zubradt, M., Washietl, S., Kellis, M. & Weissman, J. S. Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505, 701–705 (2014).

    Article  Google Scholar 

  7. Ding, Y. et al. In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature 505, 696–700 (2014).

    Article  Google Scholar 

  8. Weng, X. et al. Keth-seq for transcriptome-wide RNA structure mapping. Nat. Chem. Biol. 16, 489–492 (2020).

    Article  Google Scholar 

  9. Merino, E. J., Wilkinson, K. A., Coughlan, J. L. & Weeks, K. M. RNA structure analysis at single nucleotide resolution by selective 2′-hydroxyl acylation and primer extension (SHAPE). J. Am. Chem. Soc. 127, 4223–4231 (2005).

    Article  Google Scholar 

  10. Siegfried, N. A., Busan, S., Rice, G. M., Nelson, J. A. & Weeks, K. M. RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP). Nat. Methods 11, 959–965 (2014).

    Article  Google Scholar 

  11. Spitale, R. C. et al. Structural imprints in vivo decode RNA regulatory mechanisms. Nature 519, 486–490 (2015).

    Article  Google Scholar 

  12. Arisdakessian, C., Poirion, O., Yunits, B., Zhu, X. & Garmire, L. X. DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data. Genome Biol. 20, 211 (2019).

    Article  Google Scholar 

  13. Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).

    Article  Google Scholar 

  14. Seetin, M. G. & Mathews, D. H. RNA structure prediction: an overview of methods. Methods Mol. Biol. 905, 99–122 (2012).

    Article  Google Scholar 

  15. Mathews, D. H., Turner, D. H. & Watson, R. M. RNA secondary structure prediction. Curr. Protoc. Nucleic Acid Chem. 67, 11.12.11–11.12.19 (2016).

    Article  Google Scholar 

  16. Shi, B. et al. RNA structural dynamics regulate early embryogenesis through controlling transcriptome fate and function. Genome Biol. 21, 120 (2020).

    Article  Google Scholar 

  17. Sun, L. et al. RNA structure maps across mammalian cellular compartments. Nat. Struct. Mol. Biol. 26, 322–330 (2019).

    Article  Google Scholar 

  18. Li, W. V. & Li, J. J. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat. Commun. 9, 997 (2018).

    Article  Google Scholar 

  19. van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).

    Article  Google Scholar 

  20. Huang, M. et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat. Methods 15, 539–542 (2018).

    Article  Google Scholar 

  21. Xiong, L. et al. SCALE method for single-cell ATAC-seq analysis via latent feature extraction. Nat. Commun. 10, 4576 (2019).

    Article  Google Scholar 

  22. Qiu, J. X. et al. DeepLiDAR: Deep surface normal guided depth prediction for outdoor scene from sparse LiDAR data and single color image. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 3308–3317 (IEEE, 2019); https://doi.org/10.1109/Cvpr.2019.00343

  23. Xu, Y. et al. Depth completion from sparse LiDAR data with depth-normal constraints. In Proc. IEEE International Conference on Computer Vision 2811–2820 (IEEE, 2019); https://doi.org/10.1109/Iccv.2019.00290

  24. Tang, J., Tian, F. P., Feng, W., Li, J. & Tan, P. Learning guided convolutional network for depth completion. IEEE Trans. Image Process. 30, 1116–1129 (2021).

    Article  Google Scholar 

  25. Li, P., Shi, R. & Zhang, Q. icSHAPE-pipe: a comprehensive toolkit for icSHAPE data analysis and evaluation. Methods 178, 96–103 (2020).

    Article  Google Scholar 

  26. Lu, Z. et al. RNA duplex map in living cells reveals higher-order transcriptome structure. Cell 165, 1267–1279 (2016).

    Article  Google Scholar 

  27. He, K. M., Zhang, X. Y., Ren, S. Q. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016); https://arxiv.org/abs/1512.03385

  28. Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).

    Article  Google Scholar 

  29. Singh, J., Hanson, J., Paliwal, K. & Zhou, Y. RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nat. Commun. 10, 5407 (2019).

    Article  Google Scholar 

  30. Anger, A. M. et al. Structures of the human and Drosophila 80S ribosome. Nature 497, 80–85 (2013).

    Article  Google Scholar 

  31. Bernier, C. R. et al. RiboVision suite for visualization and analysis of ribosomes. Faraday Discuss. 169, 195–207 (2014).

    Article  Google Scholar 

  32. Bellaousov, S., Reuter, J. S., Seetin, M. G. & Mathews, D. H. RNAstructure: web servers for RNA secondary structure prediction and analysis. Nucleic Acids Res. 41, W471–W474 (2013).

    Article  Google Scholar 

  33. Mautner, S. et al. ShaKer: RNA SHAPE prediction using graph kernel. Bioinformatics 35, i354–i359 (2019).

    Article  Google Scholar 

  34. Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. In Proc. IEEE International Conference on Computer Vision 618–626 (IEEE, 2017); https://doi.org/10.1109/ICCV.2017.74

  35. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. Learning deep features for discriminative localization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2921–2929 (IEEE, 2016); https://arxiv.org/abs/1512.04150

  36. Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 19, 327–341 (2018).

    Article  Google Scholar 

  37. Lu, Z. & Chang, H. Y. The RNA base-pairing problem and base-pairing solutions. Cold Spring Harb. Perspect. Biol 10, a034926 (2018).

    Article  Google Scholar 

  38. Yan, Z. et al. Genome-wide colocalization of RNA-DNA interactions and fusion RNA pairs. Proc. Natl Acad. Sci. USA 116, 3328–3337 (2019).

    Article  Google Scholar 

  39. Luo, Z., Yang, Q. & Yang, L. RNA structure switches RBP binding. Mol. Cell 64, 219–220 (2016).

    Article  Google Scholar 

  40. Sanchez de Groot, N. et al. RNA structure drives interaction with proteins. Nat. Commun. 10, 3246 (2019).

    Article  Google Scholar 

  41. Lewis, C. J., Pan, T. & Kalsotra, A. RNA modifications and structures cooperate to guide RNA–protein interactions. Nat. Rev. Mol. Cell Biol. 18, 202–210 (2017).

    Article  Google Scholar 

  42. Huang, J. & Yin, P. Structural insights into N6-methyladenosine (m6A) modification in the transcriptome. Genomics Proteomics Bioinformatics 16, 85–98 (2018).

    Article  Google Scholar 

  43. Lukong, K. E., Chang, K. W., Khandjian, E. W. & Richard, S. RNA-binding proteins in human genetic disease. Trends Genet. 24, 416–425 (2008).

    Article  Google Scholar 

  44. Yang, Y. C. et al. CLIPdb: a CLIP-seq database for protein-RNA interactions. BMC Genomics 16, 51 (2015).

    Article  Google Scholar 

  45. Anderson, B. R., Chopra, P., Suhl, J. A., Warren, S. T. & Bassell, G. J. Identification of consensus binding sites clarifies FMRP binding determinants. Nucleic Acids Res. 44, 6649–6659 (2016).

    Article  Google Scholar 

  46. Xuan, J. J. et al. RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data. Nucleic Acids Res. 46, D327–D334 (2018).

    Article  Google Scholar 

  47. Zaccara, S., Ries, R. J. & Jaffrey, S. R. Reading, writing and erasing mRNA methylation. Nat. Rev. Mol. Cell Biol. 20, 608–624 (2019).

    Article  Google Scholar 

  48. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  Google Scholar 

  49. Garst, A. D., Edwards, A. L. & Batey, R. T. Riboswitches: structures and mechanisms. Cold Spring Harb. Perspect. Biol 3, a034926 (2011).

    Article  Google Scholar 

  50. Wan, Y. et al. Landscape and variation of RNA secondary structure across the human transcriptome. Nature 505, 706–709 (2014).

    Article  Google Scholar 

  51. Lackey, L., Coria, A., Woods, C., McArthur, E. & Laederach, A. Allele-specific SHAPE-MaP assessment of the effects of somatic variation and protein binding on mRNA structure. RNA 24, 513–528 (2018).

    Article  Google Scholar 

  52. Li, P. et al. Integrative analysis of Zika virus genome RNA structure reveals critical determinants of viral infectivity. Cell Host Microbe 24, 875–886 (2018).

    Article  Google Scholar 

  53. Zhang, Z. et al. Deep-learning augmented RNA-seq analysis of transcript splicing. Nat. Methods 16, 307–310 (2019).

    Article  Google Scholar 

  54. Flynn, R. A. et al. Transcriptome-wide interrogation of RNA secondary structure in living cells with icSHAPE. Nat. Protoc. 11, 273–290 (2016).

    Article  Google Scholar 

  55. Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    Article  Google Scholar 

  56. Andronescu, M., Bereg, V., Hoos, H. H. & Condon, A. RNA STRAND: the RNA secondary structure and statistical analysis database. BMC Bioinformatics 9, 340 (2008).

    Article  Google Scholar 

  57. Kalvari, I. et al. Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Res. 46, D335–D342 (2018).

    Article  Google Scholar 

  58. Jing, G., Kui, X. & Qiangfeng Cliff, Z. A deep learning method for recovering missing signals in transcriptome-wide RNA structure profiles from probing experiments. figshare https://doi.org/10.6084/m9.figshare.16606850 (2021).

  59. Jing, G. & Kui, X. Tsinghua-gongjing/StructureImpute: v1.0.0. Zenodo https://doi.org/10.5281/zenodo.5501018 (2021).

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant numbers 91740204, 91940306 and 31761163007 to Q.C.Z.) and the Chinese Ministry of Science and Technology (grant numbers 2019YFA0110002 and 2018YFA0107603 to Q.C.Z.). We thank the Tsinghua University Branch of China National Center for Protein Sciences (Beijing) for computational facility support.

Author information

Author notes

  1. These authors contributed equally: Jing Gong, Kui Xu.

Authors and Affiliations

  1. MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China

    Jing Gong, Kui Xu, Ziyuan Ma, Zhi John Lu & Qiangfeng Cliff Zhang

  2. Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China

    Jing Gong, Kui Xu & Qiangfeng Cliff Zhang

  3. Tsinghua-Peking Center for Life Sciences, Beijing, China

    Jing Gong, Kui Xu & Qiangfeng Cliff Zhang

  4. Shanghai Qi Zhi Institute, Shanghai, China

    Kui Xu

Authors
  1. Jing Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kui Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziyuan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhi John Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qiangfeng Cliff Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Q.C.Z. and Z.J.L. conceived and supervised the research. J.G. and K.X. designed and implemented the StructureImpute model. J.G. designed and performed all the analyses with the help of Z.M. J.G. and Q.C.Z. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Qiangfeng Cliff Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Machine Intelligence thanks Zilu Zhou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–5 and Tables 1 and 2.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gong, J., Xu, K., Ma, Z. et al. A deep learning method for recovering missing signals in transcriptome-wide RNA structure profiles from probing experiments. Nat Mach Intell 3, 995–1006 (2021). https://doi.org/10.1038/s42256-021-00412-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-021-00412-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing