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


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).

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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.

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

Code availability

Code used for training models and performing analyses are available from GitHub ( or Zenodo (


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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




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.

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The authors declare no competing interests.

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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.

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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).

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