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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