ArticleRNA (New York, N.Y.)2020
Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set.
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 13 papers.
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Who cites it
13 citing papers in PubMed.
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- Influence of contact map topology on RNA structure prediction.Nucleic acids research · 2025Article
- NucleoSeeker-precision filtering of RNA databases to curate high-quality datasets.NAR genomics and bioinformatics · 2025Article
- Exploring Evolution to Uncover Insights Into Protein Mutational Stability.Molecular biology and evolution · 2025Article
- Article
- Generating Artificial Ribozymes Using Sparse Coevolutionary Models.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Towards parsimonious generative modeling of RNA families.Nucleic acids research · 2024Article
- RNA contact prediction by data efficient deep learning.Communications biology · 2023Article
- Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks.Research square · 2023Article
- Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.Bioinformatics (Oxford, England) · 2022Article
- Getting to the bottom of lncRNA mechanism: structure-function relationships.Mammalian genome : official journal of the International Mammalian Genome Society · 2022Review
- CoCoNet-boosting RNA contact prediction by convolutional neural networks.Nucleic acids research · 2021Article
- Geometric deep learning of RNA structure.Science (New York, N.Y.) · 2021Article
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Authors and funding
4 authors.
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Abstract
RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences.
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