A workbench for the translational control of gene expression

Abstract

Ribosome profiling (Ribo-seq profiling) is the most advanced tool to study the translational control of gene expression. Unfortunately, the resolution of this cutting edge technique is severely limited by a low signal to noise ratio. To tackle this issue, we introduce here a newly designed statistical method for the identification of reproducible Ribo-seq profiles. In the case of E. coli, the analysis of 2238 Ribo-seq profiles across 9 independent datasets revealed that only 11 profiles are significantly reproducible. A subsequent data quality check led us to identify one outgroup dataset. By ruling it out, the number of highly reproducible profiles could be raised to 49. Despite its surprisingly small size, this set represents a reliable workbench to both assess the quality of the data and study the factors that influence the translation process.