Improving FRET Real-time Translation Monitoring Technology Signal-to-noise in Human Based on Ribo-Seq Modeling

Fluorescence resonance energy transfer (FRET) is a technology that enables probing of molecular interactions between pair of close molecules. Recently it was suggested and demonstrated that this technology can be used for monitoring translation in-vivo via the accurate modification of tRNA molecules. However, the signal produced in this process is very noisy.

In this project we demonstrate an approach for improving the performances of this technology: Based on the analysis of hundreds of Ribo-Seq experiments, we developed a novel approach that might improve the signal to noise (S/N) ratio, thereby enabling researching the particular gene of interest. To this end, we created a Ribo-Seq pipeline for detecting ribosomal pausing sites consistent on multiple conditions/experiments. These data is then used for finding tRNAs pairs that will be modified to optimize the FRET signal. Among others, our algorithm suggests tRNA pairs which are related to codon pairs that tend to appear near translation pauses only in the transcript of interest.