PrismNet: predicting protein-RNA interaction using in vivo RNA structural information.

ABSTRACT: Fundamental to post-transcriptional regulation, the in vivo binding of RNA binding proteins (RBPs) on their RNA targets heavily depends on RNA structures. To date, most methods for RBP-RNA interaction prediction are based on RNA structures predicted from sequences, which do not consider the various intracellular environments and thus cannot predict cell type-specific RBP-RNA interactions. Here, we present a web server PrismNet that uses a deep learning tool to integrate in vivo RNA secondary structures measured by icSHAPE experiments with RBP binding site information from UV cross-linking and immunoprecipitation in the same cell lines to predict cell type-specific RBP-RNA interactions. Taking an RBP and an RNA region with sequential and structural information as input ('Sequence & Structur

SUBMITTER: Xu Y 

PROVIDER: S-EPMC10320048 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature