DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

ABSTRACT: Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling of protein-RNA binding is critical when predicting the effects of sequence variations. Many RNA-binding proteins recognize a diverse set of motifs and binding is typically also dependent on the genomic context, making this task particularly challenging. Here, we present DeepCLIP, the first method for context-aware modeling and predicting protein binding to RNA nucleic acids using exclusively sequence data as input. We show that DeepCLIP outperforms existing methods for modeling RNA-protein binding. Importantly, we demonstrate that DeepCLIP pre

SUBMITTER: Gronning AGB 

PROVIDER: S-EPMC7367176 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature