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Understanding the basic mechanisms of transcription regulation is a major challenge in modern biology. Regulation of transcription is a complex process in which transcription factors (TFs) play a key role. Chromatin immunoprecipitation followed by high throughput sequencing is a widely and intensively used experimental technology for the identification of TF binding sites (TFBSs). Nowadays, there are tens or hundreds of ChIP-seq datasets measured for the same transcription factor. Meta-processing of such datasets into an integrated dataset is relevant. We have developed a novel method for creating these integrated datasets of TFBSs. This method consists of a three-stage application of the Rank Aggregation approach. The identified TFBSs can be sorted to further select the most reliable TFBSs. We have found a high saturation of site motifs in the most reliable TFBSs. We have also demonstrated that the most reliable TFBSs prefer to be located in open chromatin regions.

GEO database contains a lot of different experiments about transcription regulation. Most part of such experiments (ChIP-seq, DNase-seq and Histone-ChIP-seq) for 9 species (Homo sapiens, Mus musculus, Rattus norvegicus, Danio rerio, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Arabidopsis thaliana) were annotated and uniformly processed in GTRD database. Here we are describing the latest advances in these data annotations for GTRD database to formalize experimental data: hierarchical dictionaries of cell types and experimental factors. This approach helps us to integrate experimental data by cell lines (cell types and tissues) and experimental conditions as well as simplifies searching for relevant information for a user.