ArticleRNA (New York, N.Y.)2020
Assessing the accuracy of direct-coupling analysis for RNA contact prediction.
Article in RNA (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- adabmDCA 2.0-A Flexible but Easy-to-Use Package for Direct Coupling Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Integrating experimental feedback improves generative models for biological sequences.Nucleic acids research · 2025Article
- Exploring the space of self-reproducing ribozymes using generative models.Nature communications · 2025Article
- Towards parsimonious generative modeling of RNA families.Nucleic acids research · 2024Article
- pycofitness-Evaluating the fitness landscape of RNA and protein sequences.Bioinformatics (Oxford, England) · 2024Article
- trRosettaRNA: automated prediction of RNA 3D structure with transformer network.Nature communications · 2023Article
- Thirteen dubious ways to detect conserved structural RNAs.IUBMB life · 2023Article
- CoCoNet-boosting RNA contact prediction by convolutional neural networks.Nucleic acids research · 2021Article
- adabmDCA: adaptive Boltzmann machine learning for biological sequences.BMC bioinformatics · 2021Article
- Probing allosteric regulations with coevolution-driven molecular simulations.Science advances · 2021Article
- Evolutionary conservation of RNA sequence and structure.Wiley interdisciplinary reviews. RNA · 2021Review
- Evolution of frustrated and stabilising contacts in reconstructed ancient proteins.European biophysics journal : EBJ · 2021Article
- Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement.Nature communications · 2021Article
- Machine learning a model for RNA structure prediction.NAR genomics and bioinformatics · 2020Article
- DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL.Nucleic acids research · 2020Article
- Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set.RNA (New York, N.Y.) · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many noncoding RNAs are known to play a role in the cell directly linked to their structure. Structure prediction based on the sole sequence is, however, a challenging task. On the other hand, thanks to the low cost of sequencing technologies, a very large number of homologous sequences are becoming available for many RNA families. In the protein community, the idea of exploiting the covariance of mutations within a family to predict the protein structure using the direct-coupling-analysis (DCA) method has emerged in the last decade. The application of DCA to RNA systems has been limited so far. We here perform an assessment of the DCA method on 17 riboswitch families, comparing it with the commonly used mutual information analysis and with state-of-the-art R-scape covariance method. We also compare different flavors of DCA, including mean-field, pseudolikelihood, and a proposed stochastic procedure (Boltzmann learning) for solving exactly the DCA inverse problem. Boltzmann learning outperforms the other methods in predicting contacts observed in high-resolution crystal structures.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.