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

ABSTRACT: 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.

SUBMITTER: Horlacher M

PROVIDER: S-EPMC10403857 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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

Horlacher Marc M Wagner Nils N Moyon Lambert L Kuret Klara K Goedert Nicolas N Salvatore Marco M Ule Jernej J Gagneur Julien J Winther Ole O Marsico Annalisa A

Genome biology 20230804 1

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 p ...[more]

PMID: 37542318

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