ArticleNucleic acids research2019
Automated, customizable and efficient identification of 3D base pair modules with BayesPairing.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Identification of 3D motifs in Rfam with JAR3D.RNA (New York, N.Y.) · 2026Article
- All-at-once RNA folding with 3D motif prediction framed by evolutionary information.Nature methods · 2025Article
- PERFUMES: pipeline to extract RNA functional motifs and exposed structures.Bioinformatics (Oxford, England) · 2024Article
- On the predictibility of A-minor motifs from their local contexts.RNA biology · 2022Article
- Modeling and Predicting RNA Three-Dimensional Structures.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Augmented base pairing networks encode RNA-small molecule binding preferences.Nucleic acids research · 2020Article
- Advances in RNA 3D Structure Modeling Using Experimental Data.Frontiers in genetics · 2020Review
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5 authors.
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Abstract
RNA structures possess multiple levels of structural organization. A secondary structure, made of Watson-Crick helices connected by loops, forms a scaffold for the tertiary structure. The 3D structures adopted by these loops are therefore critical determinants shaping the global 3D architecture. Earlier studies showed that these local 3D structures can be described as conserved sets of ordered non-Watson-Crick base pairs called RNA structural modules. Unfortunately, the computational efficiency and scope of the current 3D module identification methods are too limited yet to benefit from all the knowledge accumulated in the module databases. We present BayesPairing, an automated, efficient and customizable tool for (i) building Bayesian networks representing RNA 3D modules and (ii) rapid identification of 3D modules in sequences. BayesPairing uses a flexible definition of RNA 3D modules that allows us to consider complex architectures such as multi-branched loops and features multiple algorithmic improvements. We benchmarked our methods using cross-validation techniques on 3409 RNA chains and show that BayesPairing achieves up to ∼70% identification accuracy on module positions and base pair interactions. BayesPairing can handle a broader range of motifs (versatility) and offers considerable running time improvements (efficiency), opening the door to a broad range of large-scale applications.
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