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

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 class...

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Detalles Bibliográficos
Autores principales: Horlacher, Marc, Wagner, Nils, Moyon, Lambert, Kuret, Klara, Goedert, Nicolas, Salvatore, Marco, Ule, Jernej, Gagneur, Julien, Winther, Ole, Marsico, Annalisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403857/
https://www.ncbi.nlm.nih.gov/pubmed/37542318
http://dx.doi.org/10.1186/s13059-023-03015-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403857/
https://www.ncbi.nlm.nih.gov/pubmed/37542318
http://dx.doi.org/10.1186/s13059-023-03015-7

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