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Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications.

Ningyuan You1, Chang Liu1, Hai Lin1

  • 1Department of Obstetrics and Gynecology of Sir Run Run Shaw Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

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|December 7, 2025
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Summary

Genomic language models (gLMs) show promise for RNA sequence analysis, outperforming task-specific methods when data is limited. Integrating biological context with data and algorithms is key for optimal performance in RNA prediction tasks.

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Area of Science:

  • Computational biology
  • Genomics
  • Molecular biology

Background:

  • RNA is crucial for cellular functions, necessitating advanced computational methods for sequence analysis.
  • Pre-trained genomic language models (gLMs) offer versatile tools for RNA-related prediction tasks.
  • Comprehensive evaluations of gLMs for RNA analysis are currently limited.

Purpose of the Study:

  • To benchmark eleven gLMs against task-specific methods for RNA sequence analysis.
  • To evaluate gLM performance across four key RNA processes: classification, m6A prediction, splicing, and translation efficiency.
  • To identify factors influencing gLM performance and provide recommendations for model selection.

Main Methods:

  • Benchmarking eleven gLMs and task-specific methods on four RNA prediction tasks.
  • Systematic profiling of pre-training datasets, input context lengths, and tokenization schemes.
  • Comparative analysis of performance based on data availability and balance.

Main Results:

  • Outstanding gLM performance is achieved by integrating biological context with data and algorithms, not just scale.
  • gLMs outperform task-specific methods in scenarios with limited or imbalanced training data.
  • Task-specific methods can be computationally efficient and achieve comparable results in certain contexts.

Conclusions:

  • gLMs hold significant promise for advancing RNA sequence analysis and biomedical research.
  • Optimizing gLM performance requires careful consideration of data, algorithms, and biological context.
  • Targeted recommendations for gLM selection can guide researchers in diverse applications.