imprint:
Springer Science and Business Media LLC, 2023
Published in:Genome Biology
Language:
English
DOI:
10.1186/s13059-023-03015-7
ISSN:
1474-760X
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>