WattmaMod enables high-resolution and extensible RNA modification profiling for nanopore direct RNA sequencing

Abstract

Nanopore direct RNA sequencing enables direct profiling of RNA modifications on native transcripts, but accurate multi-modification detection remains limited by non-stationary signals and heterogeneity across chemistries. Here, we develop WattmaMod, a deep learning framework for multi-modification detection from nanopore direct RNA sequencing data. It combines self-supervised pretraining, supervised contrastive fine-tuning, and low-label incremental adaptation to improve representation learning and support efficient extension to low-resource modification types. The framework further incorporates wavelet-guided multi-scale encoding and dynamic cross-attention fusion to model raw signals and event-level features. Results show that WattmaMod achieves robust detection of multiple RNA modifications, including m6A, m5C, m1A, A-to-I, m7G, hm5C, m1{Psi}, f5C, ac4C, m5U and {Psi}. It also extends efficiently to low-resource modification types with minimal labeled data, generalizes across sequencing chemistries and species, and predicts potential higher-order local organization among distinct RNA modifications. WattmaMod thus provides a scalable framework for high-resolution epitranscriptome profiling and expands RNA modification analysis beyond single-site prediction to coordinated multi-modification characterization.