Cited by 145RNA and protein synthesis mechanismsGenomics and Phylogenetic StudiesProtein Structure and Dynamics

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A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more

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Rivas, Elena, Lang, Raymond, Eddy, Sean R.·2012·RNA

DOI 10.1261/rna.030049.111PMC PMC3264907

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Abstract

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

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Authors: Rivas, E., Lang, R., & Eddy, S. R.
Affiliations: Howard Hughes Medical Institute (US)
Xavier University of Louisiana (US)
ORCID: Rivas, Elena
Eddy, Sean R.
Venue: RNA
Publisher: Cold Spring Harbor Laboratory
Published: 2011-12-22
Volume: 18
Issue: 2
Pages: 193-212
ISSN: 1355-8382 (ISSN-L)
1469-9001
DOI: 10.1261/rna.030049.111
PMC: PMC3264907
OpenAlex: W2109638684
Cited by: 145
References: 91
Citations by year: 2026: 9 · 2025: 20 · 2024: 16 · 2023: 15 · 2022: 17 · 2021: 5 · 2020: 4 · 2019: 5 · 2018: 5 · 2017: 3 · 2016: 6 · 2015: 7 · 2014: 7 · 2013: 17 · 2012: 7
FWCI: 4.13
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: other-oa
Locations: RNA (publishedVersion, other-oa)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
HighWire Press Open Archive (submittedVersion)
Funders: Howard Hughes Medical Institute
National Institutes of Health
National Institute of General Medical Sciences (R01GM070538)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords: RNA secondary structure prediction, probabilistic models, probabilistic context-free grammars, probabilistic generative models, overfitting, tornadoes, structural homology
MeSH terms: Algorithms, Models, Theoretical, Nucleic Acid Conformation, RNA, Software, Thermodynamics, Models, Statistical, Computational Biology, RNA Folding

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A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more

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