Abstract
Chemical modifications to mRNA respond dynamically to environmental cues and are important modulators of gene expression. Nanopore direct RNA sequencing has been applied for assessing the presence of pseudouridine (ψ) modifications through basecalling errors and signal analysis. These approaches strongly depend on the sequence context around the modification, and the occupancies derived from these measurements are not quantitative. In this work, we combine direct RNA sequencing of synthetic RNAs bearing site-specific modifications and supervised machine learning models (ModQuant) to achieve near-analytical, site-specific ψ quantification. Our models demonstrate that the ionic current signal features important for accurate ψ classification are sequence dependent and encompass information extending beyond n + 2 and n - 2 nucleotides from the ψ site. This is contradictory to current models, which assume that accurate ψ classification can be achieved with signal information confined to the 5-nucleotide k-mer window (n + 2 and n - 2 nucleotides from the ψ site). We applied our models to quantitatively profile ψ occupancy in five mRNA sites in datasets from seven human cell lines, demonstrating conserved and variable sites. Our study motivates a wider pipeline that uses ground-truth RNA control sets with site-specific modifications for quantitative profiling of RNA modifications. The ModQuant pipeline and guide are freely available at https://github.com/wanunulab/ModQuant .