AUTHOR=Ren Jinjin , Chen Xiaozhen , Zhang Zhengqian , Shi Haoran , Wu Shuxiang 

TITLE=DPred_3S: identifying dihydrouridine (D) modification on three species epitranscriptome based on multiple sequence-derived features

JOURNAL=Frontiers in Genetics

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1334132

DOI=10.3389/fgene.2023.1334132

ISSN=1664-8021

ABSTRACT=<p><bold>Introduction:</bold> Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA.</p><p><bold>Methods:</bold> To identify D in epitranscriptome, we provided a prediction framework named “DPred_3S” based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time.</p><p><bold>Results:</bold> The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for <italic>Saccharomyces cerevisiae</italic>, <italic>Escherichia coli</italic>, and <italic>Schizosaccharomyces pombe</italic>, respectively. The performances of different machine learning algorithms were also compared in this study.</p><p><bold>Discussion:</bold> The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences.</p>