Cédric Gobet

The paper investigates how selective tRNA charging defects affect translation and stress responses using inhibitors in different cell types.

Low- and high-dose anisomycin confine ribosome collisions to a dose-shifted zone just past the start codon, whereas 30 min after UV, collisions appear more dispersed and favor highly translated mRNAs.

The paper analyzes daily gene expression rhythms in the regenerating colon using single-cell transcriptomics, revealing cell-type specific patterns and identifying a resource for future studies.

The paper uses single-mRNA imaging and modeling to show that translation initiation and elongation rates are coupled, maintaining low ribosome density and translational homeostasis.

CoPhaser models biological cycles in scRNA-seq by decomposing data into periodic and non-periodic sources, revealing insights into cell cycle dynamics and other periodic processes across diverse contexts.

This paper introduces scRitmo, a method to infer circadian phases and quantify biological desynchrony from single-cell transcriptomes, showing improved performance over existing methods.

The paper introduces Riboclette, a conditional deep learning model that predicts ribosome footprints under amino acid deprivation and identifies key codons and motifs driving translation stalling.

This study shows that BCAA starvation causes codon-specific ribosome stalling, affecting translational dynamics and protein output in mammalian cells.

This study introduces RiboGL, a model combining graph and recurrent neural networks to predict ribosome density from mRNA secondary structure, outperforming existing methods.

Circadian phase inference of 914 GTEx donors reveals sex- and age-dependent rhythmic gene expression programs across 46 human tissues.

This study analyzes the circadian nuclear and cytoplasmic transcriptomes in mouse liver to understand temporal regulation of RNA processing and stability.

The paper investigates ribosome dwell times and their relationship with aminoacyl-tRNA levels in mammals, finding that codon usage and tRNA abundance influence translation efficiency.