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

Review of machine learning methods for RNA secondary structure prediction

Qi Zhao  1 , Zheng Zhao  2 , Xiaoya Fan  3 , Zhengwei Yuan  4 , Qian Mao  5   6 , Yudong Yao  7
Affiliations
Review

Review of machine learning methods for RNA secondary structure prediction

Qi Zhao et al. PLoS Comput Biol. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1
RNA primary (left), secondary (middle), and tertiary structures (right). The RNA folding process is hierarchical, i.e., the RNA secondary structure forms rapidly from linear RNA (primary structure) with a large energy loss, while the formation of a complex tertiary structure is usually much slower.
Fig 2
Fig 2. Framework for RNA secondary structure prediction methods with ML-based score schemes. Wet lab data, RNA sequence data, or RNA structure data can be employed to train an ML model to obtain a score scheme.
Using this score scheme, an RNA secondary structure can be predicted using a traditional score-based approach from a single RNA sequence.
Fig 3
Fig 3. Framework for RNA secondary structure prediction methods with ML-based preprocessing or postprocessing.
In RNA secondary structure prediction, ML models (trained by sequence data, in green) can be also used in pretreatment for selecting an appropriate prediction method or a group of appropriate parameters; ML models (trained by structure data, in brown) also can provide a means of determining the most likely structures among the outcomes.
Fig 4
Fig 4. Framework for the RNA secondary structure prediction methods with ML-based prediction process.
ML models (trained by wet lab, RNA sequence, or RNA structure data) are directly used to predict RNA secondary structures in an end-to-end way or followed by a filter or optimizer to obtain the optimal RNA secondary structure.
See this image and copyright information in PMC

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Publication types

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.