Comparative Analysis of Nanopore-based Unsupervised Learning Methods in Epitranscriptomics

RNA modifications, collectively known as epitranscriptome, are present in all kingdoms of life. To date, more than 300 types of RNA modifications have been reported. Detecting a wide range of epitranscriptomic modifications simultaneously, remains an elusive goal for supervised learning methods, and unsupervised approaches are needed. Here, we compared two main types of unsupervised approaches based on the third generation nanopore sequencing: signal-based and basecaller-error-based. Their performance was evaluated on the newly developed benchmark test dataset, constructed from recently released data comprising 25S ribosomal subunit of S. cerevisiae. The advantage of the rRNA test dataset used in our study is that it consists of native molecules containing many different modification types.