publication . Article . Other literature type . 2019

RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning.

Jaswinder Singh; Jack Hanson; Kuldip Paliwal; Yaoqi Zhou;
Open Access English
  • Published: 27 Nov 2019 Journal: Nature Communications (issn: 2041-1723, Copyright policy)
  • Publisher: Nature Publishing Group
Abstract
The limited availability of high-resolution 3D RNA structures for model training limits RNA secondary structure prediction. Here, the authors overcome this challenge by pre-training a DNN on a large set of predicted RNA structures and using transfer learning with high-resolution structures.
Subjects
free text keywords: Science, Q, Article, Computational models, Machine learning, General Biochemistry, Genetics and Molecular Biology, General Physics and Astronomy, General Chemistry
69 references, page 1 of 5

Tinoco, I, Bustamante, C. How RNA folds. J. Mol. Biol.. 1999; 293: 271-281 [OpenAIRE] [PubMed] [DOI]

Bevilacqua, PC, Ritchey, LE, Su, Z, Assmann, SM. Genome-wide analysis of RNA secondary structure. Annu. Rev. Genet.. 2016; 50: 235-266 [OpenAIRE] [PubMed] [DOI]

Tian, S, Das, R. RNA structure through multidimensional chemical mapping. Q. Rev. Biophys.. 2016; 49: e7 [PubMed] [DOI]

4.RNAcentral: a comprehensive database of non-coding RNA sequences. Nucleic Acids Res. 45, D128–D134 (2016).

Rose, PW. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res.. 2016; 45: D271-D281 [OpenAIRE] [PubMed]

Rivas, E. The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective. RNA Biol.. 2013; 10: 1185-1196 [OpenAIRE] [PubMed] [DOI]

Gutell, RR, Lee, JC, Cannone, JJ. The accuracy of ribosomal RNA comparative structure models. Curr. Opin. Struct. Biol.. 2002; 12: 301-310 [OpenAIRE] [PubMed] [DOI]

Griffiths-Jones, S, Bateman, A, Marshall, M, Khanna, A, Eddy, SR. Rfam: an RNA family database. Nucleic Acids Res.. 2003; 31: 439-441 [OpenAIRE] [PubMed] [DOI]

Zuker, M, Stiegler, P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res.. 1981; 9: 133-148 [OpenAIRE] [PubMed] [DOI]

10.Schroeder, S. J. and Turner, D. H. Chapter 17—Optical Melting Measurements of Nucleic Acid Thermodynamics. In Biophysical, Chemical, and Functional Probes of RNA Structure, Interactions and Folding: Part A, vol. 468 of Methods in Enzymology, 371–387 (Academic Press, 2009).

Lorenz, R. ViennaRNA Package 2.0. Algorithms Mol. Biol.. 2011; 6: 26 [OpenAIRE] [PubMed] [DOI]

Reuter, JS, Mathews, DH. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinforma.. 2010; 11: 129 [OpenAIRE] [DOI]

Janssen, S, Giegerich, R. The RNA shapes studio. Bioinformatics. 2014; 31: 423-425 [OpenAIRE] [PubMed] [DOI]

Do, CB, Woods, DA, Batzoglou, S. CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics. 2006; 22: e90-e98 [OpenAIRE] [PubMed] [DOI]

Sato, K, Hamada, M, Asai, K, Mituyama, T. CentroidFold: a web server for RNA secondary structure prediction. Nucleic Acids Res.. 2009; 37: W277-W280 [OpenAIRE] [PubMed] [DOI]

69 references, page 1 of 5
Abstract
The limited availability of high-resolution 3D RNA structures for model training limits RNA secondary structure prediction. Here, the authors overcome this challenge by pre-training a DNN on a large set of predicted RNA structures and using transfer learning with high-resolution structures.
Subjects
free text keywords: Science, Q, Article, Computational models, Machine learning, General Biochemistry, Genetics and Molecular Biology, General Physics and Astronomy, General Chemistry
69 references, page 1 of 5

Tinoco, I, Bustamante, C. How RNA folds. J. Mol. Biol.. 1999; 293: 271-281 [OpenAIRE] [PubMed] [DOI]

Bevilacqua, PC, Ritchey, LE, Su, Z, Assmann, SM. Genome-wide analysis of RNA secondary structure. Annu. Rev. Genet.. 2016; 50: 235-266 [OpenAIRE] [PubMed] [DOI]

Tian, S, Das, R. RNA structure through multidimensional chemical mapping. Q. Rev. Biophys.. 2016; 49: e7 [PubMed] [DOI]

4.RNAcentral: a comprehensive database of non-coding RNA sequences. Nucleic Acids Res. 45, D128–D134 (2016).

Rose, PW. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res.. 2016; 45: D271-D281 [OpenAIRE] [PubMed]

Rivas, E. The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective. RNA Biol.. 2013; 10: 1185-1196 [OpenAIRE] [PubMed] [DOI]

Gutell, RR, Lee, JC, Cannone, JJ. The accuracy of ribosomal RNA comparative structure models. Curr. Opin. Struct. Biol.. 2002; 12: 301-310 [OpenAIRE] [PubMed] [DOI]

Griffiths-Jones, S, Bateman, A, Marshall, M, Khanna, A, Eddy, SR. Rfam: an RNA family database. Nucleic Acids Res.. 2003; 31: 439-441 [OpenAIRE] [PubMed] [DOI]

Zuker, M, Stiegler, P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res.. 1981; 9: 133-148 [OpenAIRE] [PubMed] [DOI]

10.Schroeder, S. J. and Turner, D. H. Chapter 17—Optical Melting Measurements of Nucleic Acid Thermodynamics. In Biophysical, Chemical, and Functional Probes of RNA Structure, Interactions and Folding: Part A, vol. 468 of Methods in Enzymology, 371–387 (Academic Press, 2009).

Lorenz, R. ViennaRNA Package 2.0. Algorithms Mol. Biol.. 2011; 6: 26 [OpenAIRE] [PubMed] [DOI]

Reuter, JS, Mathews, DH. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinforma.. 2010; 11: 129 [OpenAIRE] [DOI]

Janssen, S, Giegerich, R. The RNA shapes studio. Bioinformatics. 2014; 31: 423-425 [OpenAIRE] [PubMed] [DOI]

Do, CB, Woods, DA, Batzoglou, S. CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics. 2006; 22: e90-e98 [OpenAIRE] [PubMed] [DOI]

Sato, K, Hamada, M, Asai, K, Mituyama, T. CentroidFold: a web server for RNA secondary structure prediction. Nucleic Acids Res.. 2009; 37: W277-W280 [OpenAIRE] [PubMed] [DOI]

69 references, page 1 of 5
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