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Towards In-Silico CLIP-seq: Predicting Protein-RNA Interaction via Sequence-to-Signal Learning

View ORCID ProfileMarc Horlacher, Nils Wagner, Lambert Moyon, View ORCID ProfileKlara Kuret, Nicolas Goedert, Marco Salvatore, View ORCID ProfileJernej Ule, View ORCID ProfileJulien Gagneur, View ORCID ProfileOle Winther, Annalisa Marsico
doi: https://doi.org/10.1101/2022.09.16.508290
Marc Horlacher
1Computational Health Center, Helmholtz Center Munich, Germany
2Department of Biology, University of Copenhagen, Denmark
3Department of Informatics, Technical University of Munich, Garching, Germany
6Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
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  • For correspondence: marc.horlacher@helmholtz-muenchen.de ole.winther@bio.ku.dk annalisa.marsico@helmholtz-muenchen.de
Nils Wagner
3Department of Informatics, Technical University of Munich, Garching, Germany
6Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
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Lambert Moyon
1Computational Health Center, Helmholtz Center Munich, Germany
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Klara Kuret
4National Institute of Chemistry, Ljubljana, Slovenia
5The Francis Crick Institute, London, UK
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Nicolas Goedert
1Computational Health Center, Helmholtz Center Munich, Germany
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Marco Salvatore
2Department of Biology, University of Copenhagen, Denmark
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Jernej Ule
4National Institute of Chemistry, Ljubljana, Slovenia
5The Francis Crick Institute, London, UK
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Julien Gagneur
1Computational Health Center, Helmholtz Center Munich, Germany
3Department of Informatics, Technical University of Munich, Garching, Germany
6Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
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Ole Winther
2Department of Biology, University of Copenhagen, Denmark
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  • For correspondence: marc.horlacher@helmholtz-muenchen.de ole.winther@bio.ku.dk annalisa.marsico@helmholtz-muenchen.de
Annalisa Marsico
1Computational Health Center, Helmholtz Center Munich, Germany
6Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
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  • For correspondence: marc.horlacher@helmholtz-muenchen.de ole.winther@bio.ku.dk annalisa.marsico@helmholtz-muenchen.de
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Abstract

Unraveling sequence determinants which drive protein-RNA interaction is crucial for studying binding mechanisms and the impact of genomic variants. While CLIP-seq allows for transcriptome-wide profiling of in vivo protein-RNA interactions, it is limited to expressed transcripts, requiring computational imputation of missing binding information. Existing classification-based methods predict binding with low resolution and depend on prior labeling of transcriptome regions for training. We present RBPNet, a novel deep learning method, which predicts CLIP 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. CLIP-seq suffers from various technical biases, complicating downstream interpretation. 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 corresponding to known binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves inference of protein-RNA interaction, as well as mechanistic interpretation of predictions.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • wagnern{at}in.tum.de, lambert.moyon{at}helmholtz-muenchen.de, klara.kuret{at}crick.ac.uk, nicolas.goedert{at}helmholtz-muenchen.de, marco.salvatore{at}bio.ku.dk, jernej.ule{at}crick.ac.uk, gagneur{at}in.tum.de

  • Revised abstract and title page layout.

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Posted September 28, 2022.
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Towards In-Silico CLIP-seq: Predicting Protein-RNA Interaction via Sequence-to-Signal Learning
Marc Horlacher, Nils Wagner, Lambert Moyon, Klara Kuret, Nicolas Goedert, Marco Salvatore, Jernej Ule, Julien Gagneur, Ole Winther, Annalisa Marsico
bioRxiv 2022.09.16.508290; doi: https://doi.org/10.1101/2022.09.16.508290
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Towards In-Silico CLIP-seq: Predicting Protein-RNA Interaction via Sequence-to-Signal Learning
Marc Horlacher, Nils Wagner, Lambert Moyon, Klara Kuret, Nicolas Goedert, Marco Salvatore, Jernej Ule, Julien Gagneur, Ole Winther, Annalisa Marsico
bioRxiv 2022.09.16.508290; doi: https://doi.org/10.1101/2022.09.16.508290

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