Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet.

ABSTRACT: RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tiss

SUBMITTER: Zhu H 

PROVIDER: S-EPMC10603054 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature