Unsupervised gene expression modeling in any given organism

Motivation: Regulation of the amount of protein that is synthesized from genes has proved to be a serious challenge in terms of analysis and prediction, and in terms of engineering and optimization, due to the large diversity in expression machinery across species.

Results: To address this challenge, we developed a methodology and a software tool (ChimeraUGEM) for predicting gene expression as well as adapting the coding sequence of a target gene to any host organism. We demonstrate these methods by predicting protein levels in 7 organisms, in 7 human tissues, and by increasing in vivo the expression of a synthetic gene up to 26-fold in the single-cell green alga C. reinhardtii.

The underlying model is designed to capture sequence patterns and regulatory signals with minimal prior knowledge on the host organism and can be applied to a multitude of species and applications.

Link for more information: https://www.cs.tau.ac.il/~tamirtul/Selected_publications/Bioinformatics2019.pdf.pdf