WORKFLOW
Wrokflow: We utilized the 169,718 human m7G sites acquired from m7GHub V2.0 and the binding regions of writers or readers from GEO dataset. Redundant sequences were removed using CD-HIT software with default parameters. Six kinds of encoding methods were considered in the project. The use of SVM is then justified by comparing four machine learning algorithms. Finally, model optimization is performed on the SVM model.
INTRODUCTION
N7-Methylguanosine (m7G) is important RNA modification at internal and the cap structure of five terminal of message RNA. We introduce a novel bioinformatics framework, m7GRegpred, designed to forecast the targets of the m7G methyltransferases (METTL1 and WDR4), and m7G readers (QKI5, QKI6, and QKI7) for the first time.
Citation: Zheng Y, Li H and Lin S. m7GRegpred: substrate prediction of N7-methylguanosine (m7G) writers and readers based on sequencing features. Front. Genet.. 2024 Vol. 15.