IRIS: A method for predicting in vivo RNA secondary structures using PARIS data

Abstract

Background: RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs, yet in vivo RNA secondary structures remain enigmatic. PARIS (Psoralen Analysis of RNA Interactions and Structures) is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in vivo. However, the existence of incompatible, fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base resolution.

Methods: We introduce IRIS, a method for predicting RNA secondary structure ensembles based on PARIS data. IRIS generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble, according to both thermodynamic principles and PARIS data.

Results: The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural data. IRIS is implemented in Python and freely available at http://iris.zhanglab.net.

Conclusion: IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure ensembles. We expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.

Graphical abstract

Keywords

RNA secondary structure / PARIS data / in vivo / structure ensembles / incompatible reads

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-020-0223-4.

AUTHOR CONTRIBUTIONS

Q.C.Z. conceived the project. T.J. and Q.C.Z. supervised the entire project. J.Z. and T.J. designed the IRIS algorithms. P.L. assisted in data collection and pre-processing and gave many critical suggestions to the methods. Q.C.Z. and T.J. proposed evaluation benchmarks. W.M. and Z.L. gave many useful suggestions. W.M. provided the support of computational resources. W.Z. and R.J. carried out a preliminary exploration of the project. All authors read and approved the final manuscript.

ACKNOWLEDGEMENTS

This work was supported by the Chinese Ministry of Science and Technology (No. 2018YFA0107603 to Q.C.Z.), the National Natural Science Foundation of China (Nos. 91740204 and 31761163007 to Q.C.Z.), the National Natural Science Foundation of China (No. 61772197 to T.J.) and the National Key Research and Development Program of China (No. 2018YFC0910404 to T.J.). Q.C.Z thanks for support from the Beijing Advanced Innovation Center for Structural Biology and the Tsinghua-Peking Joint Center for Life Sciences.

COMPLIANCE WITH ETHICS GUIDELINES

Jianyu Zhou, Pan Li, Wanwen Zeng, Wenxiu Ma, Zhipeng Lu, Rui Jiang, Qiangfeng Cliff Zhang and Tao Jiang declare that they have no conflict of interest.ƒThe article does not contain any human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

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