Predicting the translation efficiency of messenger RNA in mammalian cells.

ABSTRACT: The degree to which translational control is specified by mRNA sequence is poorly understood in mammalian cells. Here, we constructed and leveraged a compendium of 3,819 ribosomal profiling datasets, distilling them into a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing >140 human and mouse cell types. We subsequently developed RiboNN, a multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features, achieving state-of-the-art performance (r=0.79 in human and r=0.78 in mouse for mean TE across cell types). While the majority of earlier models solely considered 5' UTR sequence¹, RiboNN integrates contributions from the full-length mRNA sequence, learnin

SUBMITTER: Zheng D 

PROVIDER: S-EPMC11326250 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature