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Background <p>RNA metabolism is tightly regulated by <i>cis</i>-elements and <i>trans</i>-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.</p> Results <p>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 <i>cis</i>-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.</p> Conclusions <p>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.</p>

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A foundation language model to decipher diverse regulation of RNAs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Hanwen Zhou,
  • Yue Hu,
  • Yulong Zheng,
  • Jiefu Li,
  • Jielong Peng,
  • Jiang Hu,
  • Yun Yang,
  • Wei Chen,
  • Guoqing Zhang,
  • Zefeng Wang

摘要

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.