ArticleNature communications2024
RNA language models predict mutations that improve RNA function.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- AI foundation models for RNA biology.RNA biology · 2026Review
- De novo design of RNA pseudoknots with deep learning.bioRxiv : the preprint server for biology · 2026Article
- Machine Learning for CRISPR-Based Diagnostics.International journal of molecular sciences · 2026Review
- RNAStructuromeDB: a transcriptome-wide database of predicted RNA secondary structures with integrated APIs for functional annotation and RNA-targeted drug discovery.NAR genomics and bioinformatics · 2026Article
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- GTDB release 10: a complete and systematic taxonomy for 715 230 bacterial and 17 245 archaeal genomes.Nucleic acids research · 2026Article
- Article
- Improving RNA Secondary Structure Prediction Through Expanded Training Data.bioRxiv : the preprint server for biology · 2025Article
- In-Context Learning can distort the relationship between sequence likelihoods and biological fitness.ArXiv · 2025Article
- Article
- Article
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
- Review
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.
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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.