Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learningMore about Open Access at the Crick
Authors listMarc Horlacher Nils Wagner Lambert Moyon Klara Kuret Nicolas Goedert Marco Salvatore Jernej Ule Julien Gagneur Ole Winther Annalisa Marsico
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 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 scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.
Journal Genome Biology
Issue number 1