Review of machine learning methods for RNA secondary structure prediction

PLoS Comput Biol. 2021 Aug 26;17(8):e1009291. doi: 10.1371/journal.pcbi.1009291. eCollection 2021 Aug.

Abstract

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Computational Biology / methods
  • Machine Learning*
  • Nucleic Acid Conformation*
  • RNA / chemistry*

Substances

  • RNA

Grants and funding

This work was supported in part by the Fundamental Research Funds of Northeastern University (N181903008 - Q.Z.); the Research Start-up Fund for Talent of Dalian Maritime University (02500348 - Z.Z.); the Doctoral Scientific Research Foundation of Liaoning Province of China (2019-BS-108 - Q.M.); the Youth Seedling Project of Educational Department of Liaoning Province of China (LQN202002- Q.M.); the Fundamental Research Funds for the Central Universities (82232019 - X.Y.F.); and the National Natural Science Foundation of China (62002056 - Q.Z., 31801623 - Q.M., 81871219 - Z.W.Y). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.