ArticleNature methods2022
RNA secondary structure packages evaluated and improved by high-throughput experiments.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
What it found
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Who cites it
80 citing papers in PubMed, 130 citations in OpenAlex.
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- AWTI-Net Enables Accurate and Interpretable Functional Assessment of Disease-Associated LncRNA Mutations.Interdisciplinary sciences, computational life sciences · 2026Article
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- De novo design of RNA pseudoknots with deep learning.bioRxiv : the preprint server for biology · 2026Article
- Thermodynamic prediction of RNA cellular activity from sequence via conformational ensembles.Cell · 2026Article
- RNA Folding Nearest Neighbor Parameters Including the Modification 1-Methyl-Pseudouridine.bioRxiv : the preprint server for biology · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Diverse database and machine learning model to narrow the generalization gap in RNA structure prediction.Science advances · 2026Article
- Iterative design of a NAND hybrid riboswitch by deep batch Bayesian optimization.Nucleic acids research · 2026Article
- Translational activators align mRNAs at the small mitoribosomal subunit for translation initiation.Nature structural & molecular biology · 2026Article
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- RNA Structure and Its Function.Methods in molecular biology (Clifton, N.J.) · 2026Article
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- Integrated analysis of post-transcriptional regulations reveals insights into acute myeloid leukemia.Communications biology · 2025Article
- The Use of Enhanced Analytical Pipelines for the Characterization of Poly(A) and Poly(A)-LNP Formulation Critical Quality Attributes.Molecular pharmaceutics · 2025Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Despite the popularity of computer-aided study and design of RNA molecules, little is known about the accuracy of commonly used structure modeling packages in tasks sensitive to ensemble properties of RNA. Here, we demonstrate that the EternaBench dataset, a set of more than 20,000 synthetic RNA constructs designed on the RNA design platform Eterna, provides incisive discriminative power in evaluating current packages in ensemble-oriented structure prediction tasks. We find that CONTRAfold and RNAsoft, packages with parameters derived through statistical learning, achieve consistently higher accuracy than more widely used packages in their standard settings, which derive parameters primarily from thermodynamic experiments. We hypothesized that training a multitask model with the varied data types in EternaBench might improve inference on ensemble-based prediction tasks. Indeed, the resulting model, named EternaFold, demonstrated improved performance that generalizes to diverse external datasets including complete messenger RNAs, viral genomes probed in human cells and synthetic designs modeling mRNA vaccines.
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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.