Interpreting deep neural networks for the prediction of translation rates.

ABSTRACT:

Background

The 5' untranslated region of mRNA strongly impacts the rate of translation initiation. A recent convolutional neural network (CNN) model accurately quantifies the relationship between massively parallel synthetic 5' untranslated regions (5'UTRs) and translation levels. However, the underlying biological features, which drive model predictions, remain elusive. Uncovering sequence determinants predictive of translation output may allow us to develop a more detailed understanding of translation regulation at the 5'UTR.

Results

Applying model interpretation, we extract representations of regulatory logic from CNNs trained on synthetic and human 5'UTR reporter data. We reveal a complex interplay of regulatory sequence elements, such as initiation context and upstream

SUBMITTER: Korbel F 

PROVIDER: S-EPMC11549864 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature