Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning

Abstract

Pretrained language models have shown promise in analysing nucleotide sequences, yet a versatile model excelling across diverse tasks with a single pretrained weight set remains elusive. Here we introduce RNAErnie, an RNA-focused pretrained model built upon the transformer architecture, employing two simple yet effective strategies. First, RNAErnie enhances pretraining by incorporating RNA motifs as biological priors and introducing motif-level random masking in addition to masked language modelling at base/subsequence levels. It also tokenizes RNA types (for example, miRNA, lnRNA) as stop words, appending them to sequences during pretraining. Second, subject to out-of-distribution tasks with RNA sequences not seen during the pretraining phase, RNAErnie proposes a type-guided fine-tuning strategy that first predicts possible RNA types using an RNA sequence and then appends the predicted type to the tail of sequence to refine feature embedding in a post hoc way. Our extensive evaluation across seven datasets and five tasks demonstrates the superiority of RNAErnie in both supervised and unsupervised learning. It surpasses baselines with up to 1.8% higher accuracy in classification, 2.2% greater accuracy in interaction prediction and 3.3% improved F1 score in structure prediction, showcasing its robustness and adaptability with a unified pretrained foundation. Despite the existence of various pretrained language models for nucleotide sequence analysis, achieving good performance on a broad range of downstream tasks using a single model is challenging. Wang and colleagues develop a pretrained language model specifically optimized for RNA sequence analysis and show that it can outperform state-of-the-art methods in a diverse set of downstream tasks.