ChimeraUGEM: Unsupervised gene expression modeling in any given organism

Alon Diament, Iddo Weiner, Noam Shahar, Shira Landman, Yael Feldman, Shimshi Atar, Meital Avitan, Shira Schweitzer, Iftach Yacoby, Tamir Tuller

Research output: Contribution to journalArticlepeer-review

Abstract

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 seven organisms, in seven human tissues, and by increasing in vivo the expression of a synthetic gene up to 26-fold in the single-cell green alga Chlamydomonas 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. Availability and implementation: Source code (MATLAB, C) and binaries are freely available for download for non-commercial use at http://www.cs.tau.ac.il/~tamirtul/ChimeraUGEM/, and supported on macOS, Linux and Windows. Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)3365-3371
Number of pages7
JournalBioinformatics
Volume35
Issue number18
DOIs
StatePublished - 15 Sep 2019

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