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

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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.

Original languageEnglish
Article number180
JournalGenome Biology
Issue number1
Number of pages37
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023, BioMed Central Ltd., part of Springer Nature.

    Research areas

  • CLIP-seq, Computational biology, Deep learning, Protein-RNA interaction

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