Riboformer: a deep learning framework for predicting context-dependent translation dynamics.

ABSTRACT: Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurements of translation at the genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts in these data and identify sequence determinants of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. When trained on an unbiased dataset, Riboformer corrects experimental artifacts in previously unseen datasets, which reveals subtle differences

SUBMITTER: Shao B 

PROVIDER: S-EPMC10915169 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature