ArticleNature communications2024
Riboformer: a deep learning framework for predicting context-dependent translation dynamics.
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 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Inferring translational efficiency from transcriptomes improves noncanonical neoantigen prioritization and cancer patient stratification.NPJ precision oncology · 2026Article
- Predicting the translation efficiency of messenger RNA in mammalian cells.Nature biotechnology · 2026Article
- An expanded reference catalog of translated open reading frames for biomedical research.Nucleic acids research · 2026Article
- Conditional deep learning model reveals translation elongation determinants during amino acid deprivation.Communications biology · 2025Article
- Deep generative optimization of mRNA codon sequences for enhanced mRNA translation and therapeutic efficacy.Nature communications · 2025Article
- High-quality peptide evidence for annotating non-canonical open reading frames as human proteins.bioRxiv : the preprint server for biology · 2025Article
- Review
- AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.Molecules (Basel, Switzerland) · 2024Review
- Ribosome profiling: a powerful tool in oncological research.Biomarker research · 2024Review
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurements of translation at the genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts in these data and identify sequence determinants of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. When trained on an unbiased dataset, Riboformer corrects experimental artifacts in previously unseen datasets, which reveals subtle differences in synonymous codon translation and uncovers a bottleneck in translation elongation. Further, we show that Riboformer can be combined with in silico mutagenesis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.
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Registered trials
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