Randomized trial evaluates m6A role in translation efficiency, suggesting a novel predictive computational tool for RNA biology.
Key Points
This work aims to develop a computational tool to identify cell-specific m6A sites that regulate mRNA translation efficiency through YTHDF1 binding.
Developed a computational framework, m6ATEpre, that integrates MeRIP-seq and PAR-CLIP data.
Analyzed Ribo-seq data under YTHDF1 knockdown to identify translation-regulated genes.
Utilized an autoencoder and multilayer perceptron for predicting potential m6A sites.
m6ATEpre demonstrated superior prediction performance compared to other classifiers in various experiments.
YTHDF1-mediated m6A-reg-TE sites were identified, showing distinct properties related to translation regulation.
Integrative analysis revealed the cooperation of multiple RNA-binding proteins in m6A-dependent translation efficiency in both HeLa and HEK293T cell lines.