A foundation language model to decipher diverse regulation of RNAs
Rattachement africain : cn, us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
BACKGROUND: RNA metabolism is tightly regulated by cis-elements and trans-acting factors. Most information guiding such regulation is encoded in RNA sequences. Deciphering the regulatory rules is critical for RNA biology and therapeutics; however, the prediction of diverse regulation from RNA sequences remains a formidable challenge. RESULTS: Considering the similarities in semantic and syntactic features between RNAs and human language, we present LAMAR, a transformer-based foundation LAnguage Model for RNA Regulation, to decipher general rules underlying RNA processing. The model is pretrained on approximately 15 million sequences from both genome and transcriptome of 225 mammals and 1569 viruses, and further fine-tuned with labeled datasets for various tasks. The resulting fine-tuned models outperform the state-of-the-art methods in predicting mRNA translation efficiency and mRNA half-life, while achieving comparable accuracy to specifically designed methods in predicting splice sites of pre-mRNAs and internal ribosome entry sites (IRESs). The fine-tuned LAMAR is further applied to predict mutational effects of cis-regulatory elements and reveals known and novel regulatory elements that modulate RNA degradation. The fine-tuned LAMAR is also applied in an in silico screen of novel IRESs, resulting in the identifications of highly active IRESs that promote circRNA translation. CONCLUSIONS: Our results indicate that a single foundation language model is applicable in the comprehensive analysis of different aspects of RNA regulation and predictive identification of novel regulatory elements, providing new insight into the design and optimization of RNA drugs.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A foundation language model to decipher diverse regulation of RNAs
- Date Crossref
- 24/09/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Southern University of Science and Technology Guangming Advanced Research Institute pays non établi dans la noticeUniversité ou école supérieure
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Shanghai Institute of Nutrition and Health pays non établi dans la noticeStructure de recherche
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University of Chinese Academy of Sciences Shanghai Institute of Nutrition and Health pays non établi dans la noticeUniversité ou école supérieure
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University of North Carolina at Chapel Hill Department of Pharmacy pays non établi dans la noticeUniversité ou école supérieure
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University of North Carolina Health Care pays non établi dans la noticeÉtablissement de santé
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School of Life Sciences Department of Systems Biology pays non établi dans la noticeUniversité ou école supérieure
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CirCode Biomedicine Inc pays non établi dans la noticeEntreprise
Guangming Advanced Research Institute — Southern University of Science and Technology, Shanghai Institute of Nutrition and Health et Shanghai Institute of Nutrition and Health — University of Chinese Academy of Sciences, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.