RT Journal Article
SR Electronic
T1 Towards In-Silico CLIP-seq: Predicting Protein-RNA Interaction via Sequence-to-Signal Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.09.16.508290
DO 10.1101/2022.09.16.508290
A1 Marc Horlacher
A1 Nils Wagner
A1 Lambert Moyon
A1 Klara Kuret
A1 Nicolas Goedert
A1 Marco Salvatore
A1 Jernej Ule
A1 Julien Gagneur
A1 Ole Winther
A1 Annalisa Marsico
YR 2022
UL http://biorxiv.org/content/early/2022/09/28/2022.09.16.508290.abstract
AB 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 StatementThe authors have declared no competing interest.