Deciphering RNA modification and post-transcriptional regulation with NetRNApan.

ABSTRACT: RNA modification, which is evolutionarily conserved, is crucial for modulating various biological functions and disease pathogenesis. High resolution transcriptome-wide mapping of RNA modifications has facilitated both data resources and computational prediction of RNA modification. While these prediction algorithms are promising, they are limited in interpretability or generalizability, or the capacity for discovering novel post-transcriptional regulations. Here, we present NetRNApan, a deep learning framework for RNA modification site prediction, motif discovery and trans-regulatory factor identification. Using m5U profiles generated by FICC-seq and miCLIP-seq technologies and single-base resolution m6A sites from multiple experiments as cases, we demonstrated the accuracy of NetRNApan w

SUBMITTER: Xu H 

PROVIDER: S-EPMC12741562 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature