Accepted_test

Prediction of gene expression in prokaryotes is a fundamental bioinformatics problem that is significant for further advances in both basic research and biotechnology. The gene expression values, explicated as the amount of active protein, depend on various factors, primarily transcriptional and translational regulation. Therefore, it is important to evaluate and integrate these upstream data to accurately predict gene expression. Contextual and structural characteristics determining promoter activity have a serious impact on the transcriptional activity of the corresponding genes, while in some microorganisms it has been shown that translation elongation is the second most important factor determining the amount of protein in the cell after the mRNA level. In this regard integration of bioinformatic tools for assessing promoter activity and gene translation elongation efficiency is relevant for solving the problem of predicting microbial gene expression levels. In this work, we have been integrating software tools aimed at predicting the efficiency of various stages of gene expression, namely translation elongation and promoter activity of the corresponding genes. The designed framework allows us to expand the set of considered predictors of expression level, including both context-dependent conformational and physico-chemical properties of associated sequences and various derived indices evaluating the properties associated with the efficiency of translation elongation. The integration of these approaches with experimental expression data obtained at different levels appears promising for future gene expression prediction studies.