Predicting the translation efficiency of messenger RNA in mammalian cells.

ABSTRACT: The mechanisms by which mRNA sequences specify translational control remain poorly understood in mammalian cells. Here we generate a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing more than 140 human and mouse cell types from 3,819 ribosomal profiling datasets. We develop RiboNN, a state-of-the-art multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features. While most earlier models solely considered the 5' untranslated region (UTR) sequence, RiboNN integrates how the spatial positioning of low-level dinucleotide and trinucleotide features (that is, including codons) influences TE, capturing mechanistic principles such as how ribosomal processivity and tRNA

SUBMITTER: Zheng D 

PROVIDER: S-EPMC12323635 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature