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Horlacher, M. ; Wagner, N.* ; Moyon, L. ; Kuret, K.* ; Goedert, N. ; Salvatore, M.* ; Ule, J.* ; Gagneur, J. ; Winther, O.* ; Marsico, A.

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

Genome Biol. 24:180 (2023)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Clip-seq ; Computational Biology ; Deep Learning ; Protein-rna Interaction; Binding Protein; Sites; Specificities; Discovery; Specify; Motifs
ISSN (print) / ISBN 1474-760X
e-ISSN 1465-6906
Journal Genome Biology
Quellenangaben Volume: 24, Issue: 1, Pages: , Article Number: 180 Supplement: ,
Publisher BioMed Central
Publishing Place Campus, 4 Crinan St, London N1 9xw, England
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Wellcome Trust