MOTIVATION: Dozens of studies in recent years have demonstrated that codon usage encodes various aspects related to all stages of gene expression regulation. When relevant high-quality large-scale gene expression data are available, it is possible to statistically infer and model these signals, enabling analysing and engineering gene expression. However, when these data are not available, it is impossible to infer and validate such models. RESULTS: In this current study, we suggest Chimera-an unsupervised computationally efficient approach for exploiting hidden high-dimensional information related to the way gene expression is encoded in the open reading frame (ORF), based solely on the genome of the analysed organism. One version of the approach, named Chimera Average Repetitive Substring (ChimeraARS), estimates the adaptability of an ORF to the intracellular gene expression machinery of a genome (host), by computing its tendency to include long substrings that appear in its coding sequences; the second version, named ChimeraMap, engineers the codons of a protein such that it will include long substrings of codons that appear in the host coding sequences, improving its adaptation to a new host's gene expression machinery. We demonstrate the applicability of the new approach for analysing and engineering heterologous genes and for analysing endogenous genes. Specifically, focusing on Escherichia coli, we show that it can exploit information that cannot be detected by conventional approaches (e.g. the CAI-Codon Adaptation Index), which only consider single codon distributions; for example, we report correlations of up to 0.67 for the ChimeraARS measure with heterologous gene expression, when the CAI yielded no correlation. AVAILABILITY AND IMPLEMENTATION: For non-commercial purposes, the code of the Chimera approach can be downloaded from http://www.cs.tau.ac.il/∼tamirtul/Chimera/download.htm. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Gene expression is a central process in all living organisms. Central questions in the field are related to the way the expression levels of genes are encoded in the transcripts and affect their evolution, and the potential to predict expression levels solely by transcript features. In this study we analyze S. cerevisiae, a model organism with the most abundant relevant cellular and genomic measurements, to evaluate the accuracy in which expression levels can be predicted by different parts of the transcript. To this end, we perform various types of regression analyses based on a total of 5323 features of the transcript. The main advantage of the proposed predictors over previous ones is related to the accurate and comprehensive definitions of the relevant transcript features, which are based on biophysical knowledge of the gene transcription and translation processes, their modeling and evolution. RESULTS: Cross validation analyses of our predictors demonstrate that they achieve a correlation of 0.68/0.68/0.70/0.61/0.81 with mRNA levels, ribosomal density, protein levels, proteins per mRNA molecule (PPR), and ribosomal load (RL) respectively (all p-values <10(-140)). When we consider predictors that are based exclusively on the features related to different parts of the transcript (5'UTR, ORF, 3'UTR), the correlations with protein levels were 0.27/0.71/0.25 (all p-values <10(-5)), suggesting that the information in the UTRs is redundant, and features of the ORF alone yield similar predictions to the ones obtained based on the entire transcript. CONCLUSIONS: The reported results demonstrate that in the analyzed model organism the expression levels of a gene are encoded in the transcript. Specifically, the prediction of a large fraction of the variance of the different gene expression steps based on transcript features alone is feasible in S. cerevisiae. We report dozens of novel transcript features related to expression levels predictions, demonstrating how such analyses can aid in understanding the gene expression process and its evolution, and how such predictors can be designed for other organisms in the future.

The accepted model of eukaryotic translation initiation begins with the scanning of the transcript by the pre-initiation complex from the 5'end until an ATG codon with a specific nucleotide (nt) context surrounding it is recognized (Kozak rule). According to this model, ATG codons upstream to the beginning of the ORF should affect translation. We perform for the first time, a genome-wide statistical analysis, uncovering a new, more comprehensive and quantitative, set of initiation rules for improving the cost of translation and its efficiency. Analyzing dozens of eukaryotic genomes, we find that in all frames there is a universal trend of selection for low numbers of ATG codons; specifically, 16-27 codons upstream, but also 5-11 codons downstream of the START ATG, include less ATG codons than expected. We further suggest that there is selection for anti optimal ATG contexts in the vicinity of the START ATG. Thus, the efficiency and fidelity of translation initiation is encoded in the 5'UTR as required by the scanning model, but also at the beginning of the ORF. The observed nt patterns suggest that in all the analyzed organisms the pre-initiation complex often misses the START ATG of the ORF, and may start translation from an alternative initiation start-site. Thus, to prevent the translation of undesired proteins, there is selection for nucleotide sequences with low affinity to the pre-initiation complex near the beginning of the ORF. With the new suggested rules we were able to obtain a twice higher correlation with ribosomal density and protein levels in comparison to the Kozak rule alone (e.g. for protein levels r=0.7 vs. r=0.31; p<10(-12)).

UNLABELLED: The RFMapp is a graphical user interface application based on the RFM (ribosome flow model), enabling the estimation of the translation elongation rates of messenger ribonucleic acids (mRNAs) and the profile of ribosomal densities along the mRNAs, in a computationally efficient way. The RFMapp is based on the approach previously described by Reuveni et al., and unlike other traditional approaches in the field, which are mainly related to the genes' mean codon translation efficiency, the RFM additionally considers the codon order, the ribosomes' size and their order. Thus, it has been shown that RFM outperforms traditional predictors when analyzing both heterologous and endogenous genes. AVAILABILITY AND IMPLEMENTATION: Distributable cross-platform application and guideline are available for download at: http://www.cs.tau.ac.il/~tamirtul/RFM_Installers/install.htm.

Abstract Summary: iMAT is an Integrative Metabolic Analysis Tool, enabling the integration of transcriptomic and proteomic data with genome-scale metabolic network models to predict enzymes' metabolic flux, based on the method previously described by Shlomi et al. The prediction of metabolic fluxes based on high-throughput molecular data sources could help to advance our understanding of cellular metabolism, since current experimental approaches are limited to measuring fluxes through merely a few dozen enzymes. Availability and Implementation: http://imat.cs.tau.ac.il/ Contact: [email protected]; [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.