In the face of rapidly accumulating genomic data, our understanding of the
RNA regulatory code remains incomplete. Recent self-supervised methods in other
domains have demonstrated the ability to learn rules underlying the
data-generating process such as sentence structure in language. Inspired by
this, we extend contrastive learning techniques to genomic data by utilizing
functional similarities between sequences generated through alternative
splicing and gene duplication. Our novel dataset and contrastive objective
enable the learning of generalized RNA isoform representations. We validate
their utility on downstream tasks such as RNA half-life and mean ribosome load
prediction. Our pre-training strategy yields competitive results using linear
probing on both tasks, along with up to a two-fold increase in Pearson
correlation in low-data conditions. Importantly, our exploration of the learned
latent space reveals that our contrastive objective yields semantically
meaningful representations, underscoring its potential as a valuable
initialization technique for RNA property prediction.

通过对功能相似的基因组序列进行对比性学习，本研究提出了一种学习 RNA isoform 的通用表示方法，并在 RNA 半衰期和平均核糖体负载预测等下游任务上证实了其实用性。