Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.
Genome Biol
; 24(1): 180, 2023 08 04.
Article
en En
| MEDLINE
| ID: mdl-37542318
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.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
ARN
/
Secuencia de Bases
/
Proteínas de Unión al ARN
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Genome Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA
Año:
2023
Tipo del documento:
Article