Communications in Information and Systems

Volume 22 (2022)

Number 3

Special issue on bioinformatics and biophysics in honor of professor Michael Waterman on his 80th birthday

Guest Editors: Fengzhu Sun (University of Southern California), Guowei Wei (Michigan State University), Stephen S.-T. Yau (Tsinghua University), and Shan Zhao (University of Alabama)

A combined approach to RNA secondary structure prediction based on deep learning and minimum free energy model

Pages: 363 – 382

DOI: https://dx.doi.org/10.4310/CIS.2022.v22.n3.a4

Authors

Xiaoling He (School of Physics, Huazhong University of Science and Technology, Wuhan, China)

Xiujuan Ou (School of Physics, Huazhong University of Science and Technology, Wuhan, China)

Hong Yao (School of Physics, Huazhong University of Science and Technology, Wuhan, China)

Jun Wang (School of Physics, Huazhong University of Science and Technology, Wuhan, China)

Yi Xiao (School of Physics, Huazhong University of Science and Technology, Wuhan, China)

Abstract

The secondary structures of RNAs are the basis of building their tertiary structures and understanding their functions. Many methods of RNA secondary structure prediction have been developed and can be divided into single-sequence and multiple-sequence methods, depending on one sequence or multiple sequences as input. Here we present a method, called 2dRNAx, that combines multiplesequences method with single-sequence method. The results show that this combined method gives significantly higher accuracy than current multiple-sequences methods. Current version of the method only predicts canonical base pairs without pseudoknots, lone pairs and multiplets.

Full Text (PDF format)

The authors He, Ou, and Yau made equal contributions to this work.

Received 16 August 2021

Published 22 July 2022