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

Abstract

While 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 systematic and reproducible. While current software focuses on initial analysis steps (read mapping and counting), GRATIOSA proposes an integrated framework for subsequent analyses, providing a broad range of spatially resolved quantitative data analyses, comparisons, and representations. Several tutorials illustrate applications across diverse species for typical tasks involving RNA-Seq, ChIP-Seq, and processed Hi-C data. We also use the software to quantitatively assess the validity and extension of the twin-supercoiled domain model in Escherichia coli genome-wide transcription, using recent topoisomerase ChIP-Seq data. We show that topoisomerases are locally recruited specifically by the 40% most highly expressed transcription units, with magnitudes correlating with expression levels. The recruitment of topoisomerase I extends to around 10 kb upstream, whereas DNA gyrase is recruited at least 30 kb downstream of transcription units, with subtle requirements for each enzyme depending on the orientation and expression level.