RNAelem: an algorithm for discovering sequence-structure motifs in RNA bound by RNA-binding proteins.

ABSTRACT:

Motivation

RNA-binding proteins (RBPs) play a crucial role in the post-transcriptional regulation of RNA. Given their importance, analyzing the specific RNA patterns recognized by RBPs has become a significant research focus in bioinformatics. Deep Neural Networks have enhanced the accuracy of prediction for RBP-binding sites, yet understanding the structural basis of RBP-binding specificity from these models is challenging due to their limited interpretability. To address this, we developed RNAelem, which combines profile context-free grammar and the Turner energy model for RNA secondary structure to predict sequence-structure motifs in RBP-binding regions.

Results

RNAelem exhibited superior detection accuracy compared to existing tools for RNA sequences with structural mot

SUBMITTER: Miyake H 

PROVIDER: S-EPMC11471262 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature