Quantitative spatial analysis of bacterial transcriptome and chromosome structural data with GRATIOSA: application to twin-supercoiled domain distribution

Abstract

AbstractWhile classical models of transcriptional regulation focus on transcription factors binding at promoters, gene expression is also influenced by chromosome organization. Understanding this spatial regulation strongly benefits from integrated and quantitative spatial analyses of genome-scale data such as RNA-Seq and ChIP-Seq. We introduce Genome Regulation Analysis Tool Incorporating Organization and Spatial Architecture (GRATIOSA), a Python package making such combined analyses more automatic, systematic and reproducible. While current software focuses on initial analysis steps (read mapping and counting), GRAsTIOSA proposes an integrated framework for subsequent analyses, providing a broad range of spatially-resolved quantitative data comparisons and representations. As an example, we quantitatively assess the validity and extension of the twin-supercoiled domain model inEscherichia coligenome-wide transcription, using recent topoisomerase ChIP-Seq data. We show that topoisomerases are locally recruited by the 40% most highly expressed transcription units, with a magnitude correlating with the expression level. The recruitment of topoisomerase I extends to around 10 kb upstream, whereas DNA gyrase is recruited at least 30 kb downstream of transcription units. This organization is the primary determinant of topoisomerase I recruitment, whereas gyrase binding is additionally modulated at larger 100-200 kb length-scale. Further analyses of spatial regulation will be facilitated by GRATIOSA.