Abstract
N6-methyladenosine (m6A) is the most prevalent internal modification in mRNA and plays a critical role in post-transcriptional regulation. Despite the development of various detection methods, accurate and quantitative detection of m6A modifications at single-molecule and single-nucleotide resolution remains challenging. Many existing approaches struggle with limited resolution, inaccurate quantification, or dependence on sequence motifs. Here, we present m6Astorm, a novel computational framework for stoichiometry-preserving and stochasticity-aware identification of m6A. m6Astorm encodes the signal features (signal intensity and maximum instantaneous amplitudes derived from raw signal) and sequence context via a hybrid architecture built from convolutional neural networks and bidirectional long short-term memory networks. Trained with quantitative labels from GLORI, m6Astorm could achieve motif-independent detection of m6A modifications at single-molecule resolution by a dual-objective optimization: (i) minimizing binary cross-entropy loss for methylation state classification at molecule level, regularized by a confidence-aware penalty term suppressing low-certainty predictions; (ii) minimizing the stoichiometry bias for accurate quantitative at the nucleotide level. m6Astorm resolves co-methylation events at single-molecule, revealing coordination in m6A regulatory patterning across transcriptomes. Systematic evaluation across Hela and mouse embryonic stem cell datasets demonstrates robust cross-sample generalizability, evidenced by high prediction power (Recall), low false positive rate, accurate stoichiometric, and high area under the receiver operating characteristic curve/area under the precision-recall curve in transcriptome-wide modification profiling.