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A multi-omics dataset of heat-shock response in the yeast RNA transport protein Mip6

ABSTRACT: A multi-omics dataset of heat-shock response in the yeast RNA transport protein Mip6

PROVIDER: PRJNA559331 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR9929270_1.fastq.gz	Fastqsanger.gz
	SRR9929270_2.fastq.gz	Fastqsanger.gz
	SRR9929280_1.fastq.gz	Fastqsanger.gz
	SRR9929280_2.fastq.gz	Fastqsanger.gz
	SRR9929290.fastq.gz	Fastqsanger.gz

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A multi-omics dataset of heat-shock response in the yeast RNA transport protein Mip6

Project description:We studied the heat-shock response in wild-type (wt) and in absence of Mip6, an RNA binding protein with roles at multiple levels of the gene expression pathway. This study include; their transcriptome (RNA-seq), metabolome (targeted NMR) and H4K12ac epigenome (ChIP-seq) using an experimental desing that ensures that the same samples are used across all omics determinations. This facilitates data analysis approaches that can leverage fully matched, longitudinal, replicated data. These data will faciliate studies of covariation patterns across multiple molecular layers controlling and responding to RNA biogenesis.

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Project description:Gene expression is a biological process regulated at different molecular levels, including chromatin accessibility, transcription, and RNA maturation and transport. In addition, these regulatory mechanisms have strong links with cellular metabolism. Here we present a multi-omics dataset that captures different aspects of this multi-layered process in yeast. We obtained RNA-seq, metabolomics, and H4K12ac ChIP-seq data for wild-type and mip6Δ strains during a heat-shock time course. Mip6 is an RNA-binding protein that contributes to RNA export during environmental stress and is informative of the contribution of post-transcriptional regulation to control cellular adaptations to environmental changes. The experiment was performed in quadruplicate, and the different omics measurements were obtained from the same biological samples, which facilitates the integration and analysis of data using covariance-based methods. We validate our dataset by showing that ChIP-seq, RNA-seq and metabolomics signals recapitulate existing knowledge about the response of ribosomal genes and the contribution of trehalose metabolism to heat stress. Raw data, processed data and preprocessing scripts are made available.

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