Abstract We present RBPNet, a novel deep learning method, operation igloo white which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution.By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers.RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal.
Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences here that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis.Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.