ArticleNature methods2024
Deep generative design of RNA family sequences.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 55 citations in OpenAlex.
- RNA design across eras: from covariance models to modern generative AI.Nature reviews. Genetics · 2026Article
- A magic methyl effect in the active site of a methyltransferase ribozyme.Nature communications · 2026Article
- De novo design of RNA pseudoknots with deep learning.bioRxiv : the preprint server for biology · 2026Article
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- NEFFy: a versatile tool for computing the number of effective sequences.Bioinformatics (Oxford, England) · 2026Article
- Protein design and RNA design: Perspectives.Quantitative biology (Beijing, China) · 2026Article
- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer encoding and nucleotide assignment.Scientific reports · 2026Article
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Design prokaryotic cis-regulatory elements using language model.Nucleic acids research · 2026Article
- RNA Structure and Its Function.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- Exploring the space of self-reproducing ribozymes using generative models.Nature communications · 2025Article
- Enhanced Generalizability of RNA Secondary Structure Prediction via Convolutional Block Attention Network and Ensemble Learning.Molecules (Basel, Switzerland) · 2025Article
- Deep generative model of RNAs based on variational autoencoder with context-free grammar.Bioinformatics (Oxford, England) · 2025Article
- Computational De Novo Design of Group II Introns Yields Highly Active Ribozymes.Chembiochem : a European journal of chemical biology · 2025Article
- Design of function-regulating RNA via deep learning and AlphaFold 3.Briefings in bioinformatics · 2025Article
- The Japan-UK Synthetic Biology Conference, Spring 2025: Strengthening Global Links to Engineer Biology.ACS synthetic biology · 2025Article
- A generalized and efficient approach for complete mRNA design improves translation, stability and specificity.bioRxiv : the preprint server for biology · 2025Article
- Generative and predictive neural networks for the design of functional RNA molecules.Nature communications · 2025Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
RNA engineering has immense potential to drive innovation in biotechnology and medicine. Despite its importance, a versatile platform for the automated design of functional RNA is still lacking. Here, we propose RNA family sequence generator (RfamGen), a deep generative model that designs RNA family sequences in a data-efficient manner by explicitly incorporating alignment and consensus secondary structure information. RfamGen can generate novel and functional RNA family sequences by sampling points from a semantically rich and continuous representation. We have experimentally demonstrated the versatility of RfamGen using diverse RNA families. Furthermore, we confirmed the high success rate of RfamGen in designing functional ribozymes through a quantitative massively parallel assay. Notably, RfamGen successfully generates artificial sequences with higher activity than natural sequences. Overall, RfamGen significantly improves our ability to design functional RNA and opens up new potential for generative RNA engineering in synthetic biology.
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Registered trials
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.