Logan Persyn

Ribo-ITP identifies thousands of translons from low-input samples, revealing their regulatory roles and impact on cell growth.

The paper analyzes translational buffering across human and mouse datasets, finding conserved patterns and highlighting the role of cellular context in gene expression regulation.

The paper predicts translation efficiency (TE) of mRNA in mammalian cells using a deep learning model trained on ribosomal profiling data across multiple cell types.

The paper analyzes ribosome profiling data to show translation efficiency covariation (TEC) is conserved across human and mouse cells, revealing gene functions not evident from RNA or protein co-expression.