Y. V. Kondrakhin

Recognition of interferon-inducible sites, promoters, and enhancers

BackgroundComputational analysis of gene regulatory regions is important for prediction of functions of many uncharacterized genes. With this in mind, search of the target genes for interferon (IFN) induction appears of interest. IFNs are multi-functional cytokines. Their effects are immunomodulatory, antiviral, antibacterial, and antitumor. The interaction of the IFNs with their cell surface receptors produces an activation of several transcription factors. Four regulatory factors, ISGF3, STAT1, IRF1, and NF-κB, are essential for the function of the IFN system. The aim of this work is the development of computational approaches for the recognition of DNA binding sites for these factors and computer programs for the prediction of the IFN-inducible regions.ResultsWe developed computational approaches to the recognition of the binding sites for ISGF3, STAT1, IRF1, and NF-κB. Analysis of the distribution of these binding sites demonstrated that the regions -500 upstream of the transcription start site in IFN-inducible genes are enriched in putative binding sites for these transcription factors. Based on selected combinations of the sites whose frequencies were significantly higher than in the other functional gene groups, we developed methods for the prediction of the IFN-inducible promoters and enhancers. We analyzed 1004 sequences of the IFN-inducible genes compiled using microarray data analyses and also about 10,000 human gene sequences from the EPD and RefSeq databases; 74 of 1,664 human genes annotated in EPD were significantly IFN-inducible.ConclusionAnalyses of several control datasets demonstrated that the developed methods have a high accuracy of prediction of the IFN-inducible genes. Application of these methods to several datasets suggested that the number of the IFN-inducible genes is approximately 1500–2000 in the human genome.

CYCLONET—an integrated database on cell cycle regulation and carcinogenesis

Computational modelling of mammalian cell cycle regulation is a challenging task, which requires comprehensive knowledge on many interrelated processes in the cell. We have developed a web-based integrated database on cell cycle regulation in mammals in normal and pathological states (Cyclonet database). It integrates data obtained by ‘omics’ sciences and chemoinformatics on the basis of systems biology approach. Cyclonet is a specialized resource, which enables researchers working in the field of anticancer drug discovery to analyze the wealth of currently available information in a systematic way. Cyclonet contains information on relevant genes and molecules; diagrams and models of cell cycle regulation and results of their simulation; microarray data on cell cycle and on various types of cancer, information on drug targets and their ligands, as well as extensive bibliography on modelling of cell cycle and cancer-related gene expression data. The Cyclonet database is also accessible through the BioUML workbench, which allows flexible querying, analyzing and editing the data by means of visual modelling. Cyclonet aims to predict promising anticancer targets and their agents by application of Prediction of Activity Spectra for Substances. The Cyclonet database is available at .

Assessment of translational importance of mammalian mRNA sequence features based on Ribo-Seq and mRNA-Seq data

Ribosome profiling technology (Ribo-Seq) allowed to highlight more details of mRNA translation in cell and get additional information on importance of mRNA sequence features for this process. Application of translation inhibitors like harringtonine and cycloheximide along with mRNA-Seq technique helped to assess such important characteristic as translation efficiency. We assessed the translational importance of features of mRNA sequences with the help of statistical analysis of Ribo-Seq and mRNA-Seq data. Translationally important features known from literature as well as proposed by the authors were used in analysis. Such comparisons as protein coding versus non-coding RNAs and high- versus low-translated mRNAs were performed. We revealed a set of features that allowed to discriminate the compared categories of RNA. Significant relationships between mRNA features and efficiency of translation were also established.