Advanced deep learning strategies in nanopore RNA sequencing

The epitranscriptome comprises chemical modifications found on RNA molecules that play essential roles in co- and post-transcriptional gene regulation. Dysregulation of these modifications has been implicated in various diseases, fuelling interest in evaluating them as emerging biomarkers and therapeutic targets. Nanopore direct RNA sequencing provides a powerful platform for profiling diverse RNA modifications at single-molecule resolution, but the complexity of the signals requires advanced computational approaches for interpretation. Artificial intelligence, particularly deep learning (DL), has become central to this effort. While classical DL architectures such as convolutional and recurrent neural networks have been widely applied, more recent approaches employ specialized learning frameworks and ensemble strategies to address challenges of data scarcity, noise, and biological variability while providing higher resolution output. In this review, we summarize these developments and highlight future multidisciplinary opportunities at the intersection of artificial intelligence and biology for characterizing the epitranscriptome obtained with direct RNA nanopore sequencing.