Unsupervised detection of regulatory gene expression information in different genomic regions enables gene expression ranking

ChimeraUGEM: 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 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.

Universal evolutionary selection for high dimensional silent patterns of information hidden in the redundancy of viral genetic code

Motivation

Understanding how viruses co-evolve with their hosts and adapt various genomic level strategies in order to ensure their fitness may have essential implications in unveiling the secrets of viral evolution, and in developing new vaccines and therapeutic approaches. Here, based on a novel genomic analysis of 2625 different viruses and 439 corresponding host organisms, we provide evidence of universal evolutionary selection for high dimensional ‘silent’ patterns of information hidden in the redundancy of viral genetic code.

Results

Our model suggests that long substrings of nucleotides in the coding regions of viruses from all classes, often also repeat in the corresponding viral hosts from all domains of life. Selection for these substrings cannot be explained only by such phenomena as codon usage bias, horizontal gene transfer and the encoded proteins. Genes encoding structural proteins responsible for building the core of the viral particles were found to include more host-repeating substrings, and these substrings tend to appear in the middle parts of the viral coding regions. In addition, in human viruses these substrings tend to be enriched with motives related to transcription factors and RNA binding proteins. The host-repeating substrings are possibly related to the evolutionary pressure on the viruses to effectively interact with host's intracellular factors and to efficiently escape from the host's immune system.

Supplementary information

Supplementary data are available at Bioinformatics online.