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Genome-wide maps of chromatin state in saccharomyces cerevisiae

ABSTRACT: Genome-wide maps of chromatin state in saccharomyces cerevisiae

PROVIDER: PRJNA612040 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11291541.fastq.gz	Fastqsanger.gz
	SRR11291542.fastq.gz	Fastqsanger.gz
	SRR11291543.fastq.gz	Fastqsanger.gz
	SRR11291544.fastq.gz	Fastqsanger.gz

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Genome-wide maps of chromatin state in saccharomyces cerevisiae

Project description:We report the application of single-molecule-based sequencing technology for high-throughput profiling of histone modifications in yeast. By obtaining bases of sequence from chromatin immunoprecipitated DNA, we generated genome-wide chromatin-state maps of saccharomuces cerevisiae.We find that H3T11 phosphorylationlysine is widely distributed in gene promoter region and chromosome telomere region

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Project description:Background Microorganisms adapt their transcriptome by integrating multiple chemical and physical signals from their environment. Shake-flask cultivation does not allow precise manipulation of individual culture parameters and therefore precludes a quantitative analysis of the (combinatorial) influence of these parameters on transcriptional regulation. Steady-state chemostat cultures, which do enable accurate control, measurement and manipulation of individual cultivation parameters (e.g. specific growth rate, temperature, identity of the growth-limiting nutrient) appear to provide a promising experimental platform for such a combinatorial analysis. Results A microarray compendium of 170 steady-state chemostat cultures of the yeast Saccharomyces cerevisiae is presented and analyzed. The 170 microarrays encompass 55 unique conditions, which can be characterized by the combined settings of 10 different cultivation parameters. By applying a regression model to assess the impact of (combinations of) cultivation parameters on the transcriptome, most S. cerevisiae genes were shown to be influenced by multiple cultivation parameters, and in many cases by combinatorial effects of cultivation parameters. The inclusion of these combinatorial effects in the regression model led to higher explained variance of the gene expression patterns and resulted in higher function enrichment in subsequent analysis. We further demonstrate the usefulness of the compendium and regression analysis for interpretation of shake-flask-based transcriptome studies and for guiding functional analysis of (uncharacterized) genes and pathways. Conclusions Modeling the combinatorial effects of environmental parameters on the transcriptome is crucial for understanding transcriptional regulation. Chemostat cultivation offers a powerful tool for such an approach. Keywords: chemostat steady state samples

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Single-nucleotide resolution dynamic repair maps of UV damage in Saccharomyces cerevisiae genome

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2018-04-12 | GSE110621 | GEO

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