AUTHOR=Xu Zhongxing , Wang Xuan , Meng Jia , Zhang Lin , Song Bowen 

TITLE=m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1277099

DOI=10.3389/fmicb.2023.1277099

ISSN=1664-302X

ABSTRACT=5-Methyluridine (m 5 U) is one of the most common post-transcriptional RNA modifications, which involved in a variety of important biological processes and disease development. The precise identification of the m 5 U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here we present m5U-GEPred, a prediction framework that first to combine sequence characteristics and graph embedding-based information for m 5 U identification. The graph embedding approach was introduced to extract the global information of training data that complement the local information represented by conventional sequence features, thereby enhancing the prediction performance of m 5 U identification. m5U-GEPred outperformed the stateof-the-art m 5 U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptome, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m 5 U sites identified from Oxford Nanopore Technology (ONT) were collected