Hierarchical analysis of RNA secondary structures with pseudoknots based on sections
Ryota Masuki, Donn Liew, Ee Hou Yong
Abstract
Predicting RNA structures containing pseudoknots remains computationally challenging due to high processing costs and complexity. While standard methods for pseudoknot prediction require O(N6) time complexity, we present a hierarchical approach that significantly reduces computational cost while maintaining prediction accuracy.
Introduction
Ribonucleic acid (RNA) plays fundamental roles across all life forms, carrying genetic information, regulating gene expression, and performing various catalytic functions. Non-coding RNA, which does not translate into proteins, plays important roles in many catalytic and regulatory cellular processes [1–4]. The biological functions of non-coding RNA have strong connections with its molecular structure, particularly pseudoknots [5–8]. Thus, predicting and understanding the structure of RNA for any given base sequence is important in biology, medicine, pharmacy, and other related fields.
Materials and Methods
Definitions of terms
We define the terminologies used in the research of RNA structure.
- Primary structure of an RNA refer to its chemical sequence.
- Secondary structure of an RNA is the set of all local nested pairing of complementary bases that can be represented in a planar graph without crossing lines. Even pairings between distant bases (such as initial and final bases) are considered part of the secondary structure if they maintain this planar property.
Results and Discussion
In this work, we analyzed 726 tmRNA sequences and 454 RNase P RNA sequences from the RNAstrand database [69]. These RNA families were selected because they represent a comprehensive and diverse dataset of biologically relevant structures with well-documented pseudoknots.
Conclusion
We have presented a section-based approach to RNA pseudoknot analysis that employs mfold nearest-neighbor energy model with DPA. This method offers both computational efficiency and prediction accuracy advantages over traditional approaches. Our algorithm scales as , where n represents the number of sections and represents the average section length.
Acknowledgments
R.M., D.L., and E.H.Y thank Dr. Michael Zuker for his permission to use the mfold energy model that he developed.
Citation: Masuki R, Liew D, Yong EH (2026) Hierarchical analysis of RNA secondary structures with pseudoknots based on sections. PLoS Comput Biol 22(1): e1013904. https://doi.org/10.1371/journal.pcbi.1013904
Editor: Arli Aditya Parikesit, Indonesia International Institute for Life Sciences, INDONESIA
Received: September 22, 2025; Accepted: January 9, 2026; Published: January 27, 2026
Copyright: © 2026 Masuki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All source code used to perform the analyses in this study, along with the complete datasets, is publicly available on Github. A permanent, citable version of the code and datasets used for this publication is archived on Zenodo.
Funding: R.M. acknowledges support by the GRI programme from Nanyang Technological University and the SVAP program from The University of Tokyo. D.L. and E.H.Y. acknowledges support from Nanyang Technological University, Singapore, under its Start Up Grant Scheme (04INS000175C230), Singapore Ministry of Education through the Academic Research Fund Tier 1 (RG140/22) and Academic Research Fund Tier 2 (MOE-T2EP50223-0014). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
