Riboformer

Translation elongation is essential for maintaining cellular proteostasis and changes in the translational landscape are associated with age-related decline in protein homeostasis. We developed Riboformer, a deep learning-based framework for predicting context-dependent changes in translation dynamics using a transformer architecture. Riboformer accurately predicts ribosome densities at the codon level, corrects experimental artifacts, and identifies sequence determinants underlying ribosome collision across various biological contexts. Our tool offers a comprehensive and interpretable approach for standardizing ribosome profiling datasets and facilitating biological discoveries.