Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data

Abstract

With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devoted to those popular types such as m6A and pseudouridine. To address current limitations on studying the crucial regulator, m1A modification, we conceived this study. We have developed an integrated computational workflow designed for the detection of m1A modifications from direct RNA sequencing data. This workflow comprises a feature extractor responsible for capturing signal characteristics (such as mean, standard deviations, and length of electric signals), a single molecule-level m1A predictor trained with features extracted from the IVT dataset using classical machine learning algorithms, a confident m1A site selector employing the binomial test to identify statistically significant m1A sites, and an m1A modification rate estimator. Our model achieved accurate molecule-level prediction (Average AUC = 0.9689) and reliable m1A site detection and quantification. To show the feasibility of our workflow, we conducted a study on in vivo transcribed human HEK293 cell line, and the results were carefully annotated and compared with other techniques (i.e., Illumina sequencing-based techniques). We believed that this tool will enabling a comprehensive understanding of the m1A modification and its functional mechanisms within cells and organisms.