RNAMaRs: an interpretable framework for inferring multivalent RNA Motifs and cognate Regulators of Splicing

Abstract

Alternative splicing expands proteomic diversity and is shaped by interactions between RNA-binding proteins (RBPs) and multivalent RNA motifs. Linking sequence elements to regulatory proteins remains difficult from sequence information alone. Here we present RNAMaRs, a interpretable statistical framework that combines motif discovery with in vivo binding and splicing responses to infer motif-RBP relationships. RNAMaRs learns RBP binding principles, weights signal quality, and optimizes motif discovery in an RBP-specific manner. Across ENCODE datasets RNAMaRs consistently prioritizes the perturbed regulator, especially for large splicing effects. Independent validation in prostate cancer cells recapitulates HNRNPK binding signatures, supporting transferability across an unseen cellular context.