Open Access
Article
ML-Based RNA Secondary Structure Prediction Methods: A Survey
Qi Zhao1
, JingJing Chen1
, Zheng Zhao2
, Qian Mao3
, Haoxuan Shi1
, Xiaoya Fan4, ∗
Author Information
Submitted: 6 May 2024 | Revised: 17 Oct 2024 | Accepted: 22 Oct 2024 | Published: 29 Oct 2024
Abstract
The secondary structure of noncoding RNAs (ncRNA) is significantly related to their functions, emphasizing the importance and value of identifying ncRNA secondary structure. Computational prediction methods have been widely used in this field. However, the performance of existing computational methods has plateaued in recent years despite various advancements. Fortunately, the emergence of machine learning, particularly deep learning, has brought new hope to this field. In this review, we present a comprehensive overview of machine learning-based methods for predicting RNA secondary structures, with a particular emphasis on deep learning approaches. Additionally, we discuss the current challenges and prospects in RNA secondary structure prediction.
References
1.
Wang, D.; Farhana, A. Biochemistry, RNA Structure. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2024.
2.
Zhao, Y.; Wang, J.; Zeng, C.; Xiao, Y. Evaluation of rna secondary structure prediction for both base-pairing and topology. Biophys. Rep. 2018, 4, 123–132.
3.
Leontis, N.B.; Westhof, E. Geometric nomenclature and classification of RNA base pairs. RNA 2001, 7, 499–512.
4.
Almakarem, A.S.A.; Petrov, A.I.; Stombaugh, J.; Zirbel, C.L.; Leontis, N.B. Comprehensive survey and geometric classification of base triples in RNA structures. Nucleic Acids Res. 2012, 40, 407–1423.
5.
Doherty, E.A.; Batey, R.T.; Masquida, B.; Doudna, J.A. A universal mode of helix packing in RNA. Nat. Struct. Biol. 2001, 8, 339–343.
6.
Van Batenburg, F.H.D.; Gultyaev, A.P.; Pleij, C.W.A. PseudoBase: structural information on RNA pseudoknots. Nucleic Acids Res. 2001, 29, 194–195.
7.
Staple, D.W.; Butcher, S.E. Pseudoknots: RNA Structures with Diverse Functions. PLoS Biol. 2005, 3, e213.
8.
ENCODE Project Consortium. An Integrated Encyclopedia of DNA Elements in the Human Genome. Nature 2012, 489, 57–74.
9.
Kovalchuk, I. Chapter 24-Non-coding RNAs in genome integrity. In Genome Stability, 2nd ed.; Kovalchuk, I., Kovalchuk, O., Eds.; Volume 26 of Translational Epigenetics; Academic Press: Boston, MA, USA 2021; pp. 453–475.
10.
Kasprzyk, M.E.; Kazimierska, M.; Sura, W.; Dzikiewicz-Krawczyk, A.; Podralska, M. Chapter 3-Non-coding RNAs: Mechanisms of action. In Navigating Non-Coding RNA; Sztuba-Solinska, J., Ed.; Academic Press: Cambridge, MA, USA, 2023; pp. 89–138.
11.
Doudna, J.A.; Cech, T.R. The chemical repertoire of natural ribozymes. Nature 2002, 418, 222–228.
12.
Higgs, P.G.; Lehman, N. The RNA World: Molecular cooperation at the origins of life. Nat. Rev. Genet. 2015, 16, 7–17.
13.
Mortimer, S.A.; Kidwell, M.A.; Doudna, J.A. Insights into RNA structure and function from genome-wide studies. Nat. Rev. Genet. 2014, 15, 469–479.
14.
Meister, G.; Tuschl, T. Mechanisms of gene silencing by double-stranded RNA. Nature 2004, 431, 343–349.
15.
Serganov, A.; Nudler, E. A Decade of Riboswitches. Cell 2013, 152, 17–24.
16.
Wu, L.; Belasco, J.G. Let me count the ways: Mechanisms of gene regulation by miRNAs and siRNAs. Mol. Cell 2008, 29, 1–7.
17.
Zou, Q.; Li, J.; Hong, Q.; Lin, Z.; Wu, Y.; Shi, H.; Ju, Y. Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods. BioMed Res. Int. 2015, 2015, 810514, .
18.
Tinoco, I.; Bustamante, C. How RNA folds. J. Mol. Biol. 1999, 293, 271–281.
19.
Georgakopoulos-Soares, I.; Parada, G.E.; Hemberg, M. Secondary structures in RNA synthesis, splicing and translation. Comput. Struct. Biotechnol. J. 2022, 20, 2871–2884.
20.
Celander, D.W.; Cech, T.R. Visualizing the higher order folding of a catalytic RNA molecule. Science 1991, 251, 401–407.
21.
Stephens, Z.D.; Lee, S.Y.; Faghri, F.; Campbell, R.H.; Zhai, C.; Efron, M.J.; Iyer, R.; Schatz, M.C.; Sinha, S.; Robinson, G.E. Big Data: Astronomical or Genomical? PLoS Biol. 2015, 13, e1002195.
22.
Zarrinkar, P.P.; Williamson, J.R. Kinetic intermediates in RNA folding. Science 1994, 265, 918–924.
23.
The statistical mechanics of RNA folding. Physics 2006, 35, 218–229.
24.
Zhao, Q.; Zhao, Z.; Fan, X.; Yuan, Z.; Mao, Q.; Yao, Y. Review of machine learning methods for RNA secondary structure prediction. PLoS Comput. Biol. 2021, 17, e1009291.
25.
Condon, A. Problems on RNA Secondary Structure Prediction and Design. In Automata, Languages and Programming; (Baeten, J.C.M., Lenstra, J.K., Parrow, J., Woeginge, G., Eds.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2003; pp. 22–32.
26.
Fallmann, J.; Will, S.; Engelhardt, J.; Gruning, B.; Backofen, R.; Stadler, P.F. Recent advances in RNA folding. J. Biotechnol. 2017, 261, 97–104.
27.
Seetin, M.G.; Mathews, D.H. RNA structure prediction: An overview of methods. Methods Mol. Biol. 2012, 905, 99–122.
28.
Furtig, B.; Richter, C.; Wohnert, J.; Schwalbe, H. NMR spectroscopy of RNA. ChemBioChem 2003, 4, 936–962.
29.
Westhof, E. Twenty years of RNA crystallography. RNA 2015, 21, 486–487.
30.
Tijerina, P.; Mohr, S.; Russell, R. DMS Footprinting of Structured RNAs and RNA-Protein Complexes. Nat. Protoc. 2007, 2, 2608–2623.
31.
Wilkinson, K.A.; Merino, E.J.; Weeks, K.M. Selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE): Quantitative RNA structure analysis at single nucleotide resolution. Nat. Protoc. 2006, 1, 1610– 1616.
32.
Kertesz, M.; Wan, Y.; Mazor, E.; Rinn, J.L.; Nutter, R.C.; Chang, H.Y.; Segal, E. Genome-wide Measurement of RNA Secondary Structure in Yeast. Nature 2010, 467, 9322.
33.
Underwood, J.G.; Uzilov, A.V.; Katzman, S.; Onodera, C.S.; Mainzer, J.E.; Mathews, D.H.; Lowe, T.M.; Salama, S.R.; Haussler, D. FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat. Methods 2010, 7, 995–1001.
34.
Bevilacqua, P.C.; Ritchey, L.E.; Su, Z.; Assmann, S.M. Genome-Wide Analysis of RNA Secondary Structure. Annu. Rev. Genet. 2016, 50, 235–266.
35.
Tian, S.; Das, R. RNA structure through multidimensional chemical mapping. Q. Rev. Biophys. 2016, 49, e7.
36.
RNAcentral: A comprehensive database of non-coding RNA sequences. Nucleic Acids Res. 2017, 45, D128– D134.
37.
Gutell, R.R.; Lee, J.C.; Cannone, J.J. The accuracy of ribosomal RNA comparative structure models. Curr. Opin. Struct. Biol. 2002, 12, 301–310.
38.
Madison, J.T.; Everett, G.A.; Kung, H. Nucleotide sequence of a yeast tyrosine transfer RNA. Science 1966, 153, 531–534.
39.
Gutell, R.R.; Weiser, B.; Woese, C.R.; Noller, H.F. Comparative anatomy of 16-S-like ribosomal RNA. Prog. Nucleic Acid Res. Mol. Biol. 1985, 32, 155–216.
40.
Ruan, J.; Stormo, G.D.; Zhang, W. An iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots. Bioinformatics 2004, 20, 58–66.
41.
Hofacker, I.L.; Fekete, M.; Flamm, C.; Huynen, M.A.; Rauscher, S.; Stolorz, P.E.; Stadler, P.F. Automatic detection of conserved RNA structure elements in complete RNA virus genomes. Nucleic Acids Res. 1998, 26, 3825–3836.
42.
Bindewald, E.; Shapiro, B.A. Rna secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers. RNA 2006, 12, 342–352.
43.
Legendre, A.; Angel, E.; Tahi, F. Bi-objective integer programming for RNA secondary structure prediction with pseudoknots. BMC Bioinformatics 2018, 19, 1–15.
44.
Han, K.; Kim, H.J. Prediction of common folding structures of homologous RNAs. Nucleic Acids Res. 1993, 21, 1251–1257.
45.
Tahi, F.; Gouy, M.; Regnier, M. Automatic RNA secondary structure prediction with a comparative approach. Comput. Chem. 2002, 26, 521–530.
46.
Nussinov, R.; Jacobson, A.B. Fast algorithm for predicting the secondary structure of single-stranded RNA. Proc. Natl. Acad. Sci. USA 1980, 77, 6309–6313.
47.
Engelen, S.; Tahi, F. Tfold: Efficient in silico prediction of non-coding RNA secondary structures. Nucleic Acids Res. 2010, 38, 2453–2466.
48.
Bellaousov, S.; Mathews, D.H. ProbKnot: Fast prediction of RNA secondary structure including pseudoknots. RNA 2010, 16, 1870–1880.
49.
Burge, S.W.; Daub, J.; Eberhardt, R.; Tate, J.; Barquist, L.; Nawrocki, E.P.; Eddy, S.R.; Gardner, P.P.; Bateman, A. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 2013, 41, D226–D232.
50.
Xia, T.; SantaLucia, J.; Burkard, M.E.; Kierzek, R.; Schroeder, S.J.; Jiao, X.; Cox, C.; Turner, D.H. Thermody- namic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 1998, 37, 14719–14735.
51.
Mathews, D.H.; Sabina, J.; Zuker, M.; Turner, D.H. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 1999, 288, 911–940.
52.
Andronescu, M.; Condon, A.; Turner, D.H.; Mathews, D.H. The determination of RNA folding nearest neighbor parameters. Methods Mol. Biol. 2014, 1097, 45–70.
53.
Turner, D.H.; Mathews, D.H. NNDB: The nearest neighbor parameter database for predicting stability of nucleic acid secondary structure. Nucleic Acids Res. 2010, 38, D280–D282.
54.
Bon, M.; Micheletti, C.; Orland, H. McGenus: A Monte Carlo algorithm to predict RNA secondary structures with pseudoknots. Nucleic Acids Res. 2013, 41, 1895–1900.
55.
Reeder, J.; Giegerich, R. Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics. BMC Bioinform. 2004, 5, 104.
56.
Dirks, R.M.; Pierce, N.A. A partition function algorithm for nucleic acid secondary structure including pseudoknots. J. Comput. Chem. 2003, 24, 1664–1677.
57.
Rivas, E.; Eddy, S.R. A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol. 1999, 285, 2053–2068.
58.
Sato, K.; Kato, Y. Prediction of RNA secondary structure including pseudoknots for long sequences. Brief. Bioinform. 2021, 23, bbab395.
59.
Poolsap, U.; Kato, Y.; Akutsu, T. Prediction of RNA secondary structure with pseudoknots using integer programming. BMC Bioinformatics 2009, 10, 1–11.
60.
Jordan, M.I.; Mitchell, T.M. Machine learning: Trends, perspectives, and prospects. Science 2015, 349, 255–260.
61.
Lorenz, R.; Bernhart, S.H.; Siederdissen, C.H.Z.; Tafer, H.; Flamm, C.; Stadler, P.F.; Hofacker, I.L. ViennaRNA Package 2.0. Algorithms Mol. Biol. 2011, 6, 26.
62.
Bellaousov, S.; Reuter, J.S.; Seetin, M.G.; Mathews, D.H. RNAstructure: web servers for RNA secondary structure prediction and analysis. Nucleic Acids Res. 2013, 41, W471–W474.
63.
Andronescu, M.; Condon, A.; Hoos, H.H.; Mathews, D.H.; Murphy, K.P. Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics 2007, 23, i19–i28.
64.
Washietl, S.; Will, S.; Hendrix, D.A.; Goff, L.A.; Rinn, J.L.; Berger, B.; Kellis, M. Computational analysis of noncoding RNAs. Wiley Interdiscip. Rev. RNA 2012, 3, 759–778.
65.
Tang, X.; Thomas, S.; Tapia, L.; Giedroc, D.P.; Amato, N.M. Simulating RNA folding kinetics on approximated energy landscapes. J. Mol. Biol. 2008, 381, 1055–1067.
66.
Rehmsmeier, M.; Steffen, P.; Hochsmann, M.; Giegerich, R. Fast and effective prediction of microRNA/target duplexes. RNA 2004, 10, 1507–1517.
67.
Zakov, S.; Goldberg, Y.; Elhadad, M.; Ziv-Ukelson, M. Rich parameterization improves RNA structure prediction. J. Comput.Biol. A J. Comput. Mol. Cell Biol. 2011, 18, 1525–1542.
68.
Akiyama, M.; Sato, K.; Sakakibara, Y. A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model. J. Bioinform. Comput. Biol. 2018, 16, 1840025.
69.
Sato, K.; Akiyama, M.; Sakakibara, Y. Rna secondary structure prediction using deep learning with thermody- namic integration. Nat. Commun. 2021, 12, 941.
70.
Akakibara, Y.; Brown, M.; Hughey, R.; Mian, I.S.; Sjolander, K.; Underwood, R.C.; Haussler, D. Stochastic context-free grammars for tRNA modeling. Nucleic Acids Res. 1994, 22, 5112–5120.
71.
Woodson, S.A. Recent insights on RNA folding mechanisms from catalytic RNA. Cell. Mol. Life Sci. 2000, 57, 796–808.
72.
Knudsen, B.; Hein, J. RNA secondary structure prediction using stochastic context-free grammars and evolutionary history. Bioinformatics 1999, 15, 446–454.
73.
Dowell, R.D.; Eddy, S.R. Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction. BMC Bioinform. 2004, 5, 71.
74.
Rivas, E.; Lang, R.; Eddy, S.R. A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more. RNA 2012, 18, 193–212.
75.
Sato, K.; Hamada, M.; Mituyama, T.; Asai, K.; Sakakibara, Y. A non-parametric bayesian approach for predicting RNA secondary structures. J. Bioinform. Comput. Biol. 2010, 8, 727–742.
76.
Yonemoto, H.; Asai, K.; Hamada, M. A semi-supervised learning approach for RNA secondary structure prediction. Comput. Biol. Chem. 2015, 57, 72–79.
77.
Do, C.B.; Woods, D.A.; Batzoglou, S. CONTRAfold: RNA secondary structure prediction without physics- based models. Bioinformatics 2006, 22, e90–e98.
78.
Hor, C.-Y.; Yang, C.-B.; Chang, C.-H.; Tseng, C.-T.; Chen, H.-H. A tool preference choice method for RNA secondary structure prediction by SVM with statistical tests. Evol. Bioinform. 2013, 9, EBO–S10580.
79.
Zhu, Y.; Xie, Z.; Li, Y.; Zhu, M.; Chen, Y.-P.P. Research on folding diversity in statistical learning methods for RNA secondary structure prediction. Int. J. Biol. Sci. 2018, 14, 872–882.
80.
Singh, J.; Hanson, J.; Paliwal, K.; Zhou, Y. RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nat. Commun. 2019, 10, 5407.
81.
Haynes, T.; Knisley, D.; Knisley, J. Using a neural network to identify secondary RNA structures quantified by graphical invariants. Commun. Math. Comput. Chem. 2008, 60, 277–290.
82.
Koessler, D.R.; Knisley, D.J.; Knisley, J.; Haynes, T. A predictive model for secondary RNA structure using graph theory and a neural network. BMC Bioinform. 2010, 11, S21.
83.
Takefuji, Y.; Chen, L.L.; Lee, K.C.; Huffman, J. Parallel algorithms for finding a near-maximum independent set of a circle graph. IEEE Trans. Neural Netw. 1990, 1, 263–267.
84.
Qasim, R.; Kauser, N.; Jilani, T. Secondary Structure Prediction of RNA using Machine Learning Method. Int. J. Comput. Appl. 2010, 10, 15–22.
85.
Liu, Q.; Ye, X.; Zhang, Y. A Hopfield Neural Network Based Algorithm for RNA Secondary Structure Prediction. In Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS’06), Hangzhou, China, 20–24 June 2006; Volume 1, pp. 10–16.
86.
Apolloni, B.; Lotorto, L.; Morpurgo, A.; Zanaboni, A.M. RNA Secondary Structure Prediction by MFT Neural Networks. Psychol. Forsch. 2003, 2003, 143–148.
87.
Steeg, E.W. Neural Networks, Adaptive Optimization, and RNA Secondary Structure Prediction; American Association for Artificial Intelligence: Palo Alto, CA, USA, 1993; pp. 121–160.
88.
Singh, J.; Paliwal, K.; Zhang, T.; Singh, J.; Litfin, T.; Zhou, Y. Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning. Bioinformatics 2021, 37, 2589–2600.
89.
Fu, L.; Cao, Y.; Wu, J.; Peng, Q.; Nie, Q.; Xie, X. UFold: Fast and accurate RNA secondary structure prediction with deep learning. Nucleic Acids Res. 2022, 50, e14.
90.
Chen, X.; Li, Y.; Umarov, R.; Gao, X.; Song, L. RNA Secondary Structure Prediction By Learning Unrolled Algorithms. arXiv 2020, arXiv:2002.05810.
91.
Calonaci, N.; Jones, A.; Cuturello, F.; Sattler, M.; Bussi, G. Machine learning a model for RNA structure prediction. NAR Genom. Bioinform. 2020, 2, lqaa090.
92.
Wu, H.; Tang, Y.; Lu, W.; Chen, C.; Huang, H.; Fu, Q. RNA Secondary Structure Prediction Based on Long Short-Term Memory Model. In Intelligent Computing Theories and Application; Huang, D.-S., Bevilacqua, V., Premaratne, P., Gupta, P., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Cham, Switzerland, 2018; pp. 595–599.
93.
Lu, W.; Tang, Y.; Wu, H.; Huang, H.; Fu, Q.; Qiu, J.; Li, H. Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter. BMC Bioinform. 2019, 20, 684.
94.
Quan, L.; Cai, L.; Chen, Y.; Mei, J.; Sun, X.; Lyu, Q. Developing parallel ant colonies filtered by deep learned constrains for predicting RNA secondary structure with pseudo-knots. Neurocomputing 2020, 384, 104–114.
95.
Fei, Y.; Zhang, H.; Wang, Y.; Liu, Z.; Liu, Y. LTPConstraint: a transfer learning based end-to-end method for RNA secondary structure prediction. BMC Bioinformatics 2022, 23, 354.
96.
Hochreiter, S. Long Short-Term Memory; Neural Computation MIT-Press: Cambridge, MA, USA, 1997.
97.
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; Polosukhin, I. Attention is all you need. In Advances in Neural Information Processing Systems; Curran Associates, Inc.: Red Hook, NY, USA, 2017.
98.
Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv 2015, arXiv:1505.04597.
99.
Booy, M.; Ilin, A.; Orponen, P. RNA secondary structure prediction with convolutional neural networks. BMC Bioinformatics 2022, 23, 58.
100.
Zhang, H.; Zhang, C.; Li, Z.; Li, C.; Wei, X.; Zhang, B.; Liu, Y. A New Method of RNA Secondary Structure Prediction Based on Convolutional Neural Network and Dynamic Programming. Front. Genet. 2019, 10, 467.
101.
Wang, L.; Liu, Y.; Zhong, X.; Liu, H.; Lu, C.; Li, C.; Zhang, H. Dmfold: A novel method to predict rna secondary structure with pseudoknots based on deep learning and improved base pair maximization principle. Front. Genet. 2019, 10, 143.
102.
Willmott, D.; Murrugarra, D.; Ye, Q. Improving RNA secondary structure prediction via state inference with deep recurrent neural networks. Comput. Math. Biophys. 2020, 8, 36–50.
103.
Chen, C.C.; Chan, Y.M. REDfold: Accurate RNA secondary structure prediction using residual encoder- decoder network. BMC Bioinform. 2023, 24, 122.
104.
Andronescu, M.; Bereg, V.; Hoos, H.H.; Condon, A. RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database. BMC Bioinform. 2008, 9, 340.
105.
Burley, S.K.; Bhikadiya, C.; Bi, C.; Bittrich, S.; Chen, L.; Crichlow, G.V.; Christie, C.H.; Dalenberg, K.; Costanzo, L.D.; Duarte, J.M.; et al. RCSB Protein Data Bank: Powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2020, 49, D437–D451.
106.
Danaee, P.; Rouches, M.; Wiley, M.; Deng, D.; Huang, L.; Hendrix, D. bprna: large-scale automated annotation and analysis of rna secondary structure. Nucleic Acids Res. 2018, 46, 5381–5394.
107.
Rnacentral 2021: Secondary structure integration, improved sequence search and new member databases. Nucleic Acids Res. 2021, 49, D212–D220.
108.
Sweeney, B.A.; Hoksza, D.; Nawrocki, E.P.; Ribas, C.E.; Madeira, F.; Cannone, J.J.; Gutell, R.; Maddala, A.; Meade, C.D.; Williams, L.D.; et al. R2DT is a framework for predicting and visualising RNA secondary structure using templates. Nat. Commun. 2021, 12, 3494.
109.
Juhling, F.; Morl, M.; Hartmann, R.K.; Sprinzl, M.; Stadler, P.F.; Putz, J. tRNAdb 2009: Compilation of tRNA sequences and tRNA genes. Nucleic Acids Res. 2009, 37, D159–D162.
110.
Gutell, R.R. Collection of small subunit (16S-and 16S-like) ribosomal RNA structures. Nucleic Acids Res. 1994, 22(17), 3502–3507.
111.
Zwieb, C.; Gorodkin, J.; Knudsen, B.; Burks, J.; Wower, J. tmRDB (tmRNA database). Nucleic Acids Res. 2003, 31, 446–447.
112.
Richardson, K.E.; Kirkpatrick, C.C.; Znosko, B.M. RNA CoSSMos 2.0: An improved searchable database of secondary structure motifs in RNA three-dimensional structures. Database J. Biol. Databases Curation 2020, 2020, baz153.
113.
Korunes, K.L.; Myers, R.B.; Hardy, R.; Noor, M.A.F. PseudoBase: a genomic visualization and exploration resource for the Drosophila pseudoobscura subgroup. Fly 2021, 15, 38–44.
114.
Nagaswamy, U.; Larios-Sanz, M.; Hury, J.; Collins, S.; Zhang, Z.; Zhao, Q.; Fox, G.E. NCIR: A database of non-canonical interactions in known RNA structures. Nucleic Acids Res. 2002, 30, 395–397.
115.
Sloma, M.F.; Mathews, D.H. Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures. RNA 2016, 22, 1808–1818.
116.
Tan, Z.; Fu, Y.; Sharma, G.; Mathews, D.H. TurboFold II: RNA structural alignment and secondary structure prediction informed by multiple homologs. Nucleic Acids Res. 2017, 45, 11570–11581.
117.
Kalvari, I.; Nawrocki, E.P.; Ontiveros-Palacios, N.; Argasinska, J.; Lamkiewicz, K.; Marz, M.; Griffiths-Jones, S.; Toffano-Nioche, C.; Gautheret, D.; Weinberg, Z.; et al. Rfam 14: Expanded coverage of metagenomic, viral and microrna families. Nucleic Acids Res. 2021, 49, D192–D200.
118.
Wolfinger, M.T.; Svrcek-Seiler, W.A.; Flamm, C.; Hofacker, I.L.; Stadler, P.F. Efficient computation of RNA folding dynamics. J. Phys. A: Math. Gen. 2004, 37, 4731.
119.
Gruber, A.R.; Findeiß, S.; Washietl, S.; Hofacker, I.L.; Stadler, P.F. RNAz 2.0: Improved noncoding RNA detection. Pac. Symp. Biocomputing. 2010, 2010, 69–79.
120.
Moulton, V. Tracking down noncoding RNAs. Proc. Natl. Acad. Sci. USA 2005, 102, 2269–2270.
121.
Lu, Z.J.; Mathews, D.H. Efficient siRNA selection using hybridization thermodynamics. Nucleic Acids Res. 2008, 36, 640–647.
122.
Tafer, H.; Ameres, S.L.; Obernosterer, G.; Gebeshuber, C.A.; Schroeder, R.; Martinez, J.; Hofacker, I.L. The impact of target site accessibility on the design of effective siRNAs. Nat. Biotechnol. 2008, 26, 578–583.
123.
Sazani, P.; Gemignani, F.; Kang, S.-H.; Maier, M.A.; Manoharan, M.; Persmark, M.; Bortner, D.; Kole, R. Systemically delivered antisense oligomers upregulate gene expression in mouse tissues. Nat. Biotechnol. 2002, 20, 1228–1233.
124.
Childs-Disney, J.L.; Wu, M.; Pushechnikov, A.; Aminova, O.; Disney, M.D. A small molecule microarray platform to select RNA internal loop-ligand interactions. ACS Chem. Biol. 2007, 2, 745–754.
125.
Palde, P.B.; Ofori, L.O.; Gareiss, P.C.; Lerea, J.; Miller, B.L. Strategies for Recognition of Stem-loop RNA Structures by Synthetic Ligands: Application to the HIV-1 Frameshift Stimulatory Sequence. J. Med. Chem. 2010, 53, 6018–6027.
126.
Castanotto, D.; Rossi, J.J. The promises and pitfalls of RNA-interference-based therapeutics. Nature 2009, 457, 426–433.
127.
Gareiss, P.C.; Sobczak, K.; McNaughton, B.R.; Palde, P.B.; Thornton, C.A.; Miller, B.L. Dynamic Com- binatorial Selection of Molecules Capable of Inhibiting the (CUG) Repeat RNA – MBNL1 Interaction in vitro: Discovery of Lead Compounds Targeting Myotonic Dystrophy (DM1). J. Am. Chem. Soc. 2008, 130, 16254–16261.
128.
Rouillard, J.M.; Zuker, M.; Gulari, E. OligoArray 2.0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Res. 2003, 31, 3057–3062.
129.
Tavares, R.D.C.A.; Mahadeshwar, G.; Wan, H.; Huston, N.C.; Pyle, A.M. The Global and Local Distribution of RNA Structure throughout the SARS-CoV-2 Genome. J. Virol. 2021, 95, e02190-20.
130.
Vandelli, A.; Monti, M.; Milanetti, E.; Armaos, A.; Rupert, J.; Zacco, E.; Bechara, E.; Ponti, R.D.; Tartaglia, G.G. Structural analysis of SARS-CoV-2 genome and predictions of the human interactome. Nucleic Acids Res. 2020, 48, 11270–11283.
131.
Wang, X.; Gu, R.; Chen, Z.; Li, Y.; Ji, X.; Ke, G.; Wen, H. Uni-Rna: Universal Pre-Trained Models Revolutionize Rna Research. bioRxiv 2023, 2023, 548588.
132.
Chen, J.; Hu, Z.; Sun, S.; Tan, Q.; Wang, Y.; Yu, Q.; Zong, L.; Hong, L.; Xiao, J.; Shen, T.; et al. Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions. arXiv 2022, arXiv:2204.00300.
133.
Akiyama, M.; Sakakibara, Y. Informative rna base embedding for rna structural alignment and clustering by deep representation learning. NAR Genom. Bioinform. 2022, 4, lqac012.
134.
Zhang, J.; Fei, Y.; Sun, L.; ; Zhang, Q.C. Advances and opportunities in RNA structure experimental determination and computational modeling. Nat. Methods 2022, 19, 1193–1207.
135.
Lyngsø, R.B.; Pedersen, C.N. RNA pseudoknot prediction in energy-based models. J. Comput. Biol. 2000, 7, 409–427.
136.
Parisien, M.; Major, F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 2008, 452, 51–55.
Issue
Volume 1, Issue 1How to Cite
Zhao, Q., Chen, J., Zhao, Z., Mao, Q., Shi, H., & Fan, X. (2024). ML-Based RNA Secondary Structure Prediction Methods: A Survey. AI Medicine, 1(1), 6. https://doi.org/10.53941/aim.2024.100006
RIS
BibTex
Copyright & License
Copyright (c) 2024 by the authors.
This work is licensed under a This work is licensed under a Creative Commons Attribution 4.0 International License.