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Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.
Genome Biol. 24:180 (2023)
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
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,
Article Number: 180
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