ArticlePLoS computational biology2021
Predicting mean ribosome load for 5'UTR of any length using deep learning.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 57 citations in OpenAlex.
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- Predicting the translation efficiency of messenger RNA in mammalian cells.Nature biotechnology · 2026Article
- VaxLab: integrated platform for rapid multistrategy mRNA vaccine design.Experimental & molecular medicine · 2026Article
- UTR-DynaPro: a CNN-transformer multimodal language model for decoding 5'UTR regulatory mechanisms.Scientific reports · 2026Article
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- Recent Progress in Sequence Optimization of mRNA Vaccine: Biological Mechanism, Quantitative Metrics, and Computational Model.Bioinformatics and biology insights · 2026Review
- mRNABERT: advancing mRNA sequence design with a universal language model and comprehensive dataset.Nature communications · 2025Article
- Enhancing mRNA translation efficiency with discriminative and generative artificial intelligence by optimizing 5' UTR sequences.iScience · 2025Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- Programming human cell type-specific gene expression via an atlas of AI-designed enhancers.bioRxiv : the preprint server for biology · 2025Article
- Machine learning-based analysis of the impact of 5' untranslated region on protein expression.Nucleic acids research · 2025Article
- mRNABench: A curated benchmark for mature mRNA property and function prediction.bioRxiv : the preprint server for biology · 2025Article
- The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis.Developmental cell · 2025Article
- From computational models of the splicing code to regulatory mechanisms and therapeutic implications.Nature reviews. Genetics · 2025Review
- UTR-Insight: integrating deep learning for efficient 5' UTR discovery and design.BMC genomics · 2025Article
- UTRGAN: learning to generate 5' UTR sequences for optimized translation efficiency and gene expression.Bioinformatics advances · 2025Article
- mRNA vaccine sequence and structure design and optimization: Advances and challenges.The Journal of biological chemistry · 2025Review
- A generative framework for enhanced cell-type specificity in rationally designed mRNAs.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The 5' untranslated region plays a key role in regulating mRNA translation and consequently protein abundance. Therefore, accurate modeling of 5'UTR regulatory sequences shall provide insights into translational control mechanisms and help interpret genetic variants. Recently, a model was trained on a massively parallel reporter assay to predict mean ribosome load (MRL)-a proxy for translation rate-directly from 5'UTR sequence with a high degree of accuracy. However, this model is restricted to sequence lengths investigated in the reporter assay and therefore cannot be applied to the majority of human sequences without a substantial loss of information. Here, we introduced frame pooling, a novel neural network operation that enabled the development of an MRL prediction model for 5'UTRs of any length. Our model shows state-of-the-art performance on fixed length randomized sequences, while offering better generalization performance on longer sequences and on a variety of translation-related genome-wide datasets. Variant interpretation is demonstrated on a 5'UTR variant of the gene HBB associated with beta-thalassemia. Frame pooling could find applications in other bioinformatics predictive tasks. Moreover, our model, released open source, could help pinpoint pathogenic genetic variants.
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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.