Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

Blog Article

Abstract We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution.By training on up Stools 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 that correspond to known and novel binding motifs and enables variant-impact Bobby Pins scoring via in silico mutagenesis.

Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.