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TempO-Seq S1500+ Platform Gene Expression of Rat Liver Mode of Action Samples

ABSTRACT: TempO-Seq S1500+ Platform Gene Expression of Rat Liver Mode of Action Samples

PROVIDER: PRJNA487528 | ENA |

REPOSITORIES: ENA

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Dataset's files

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			Action	DRS
	SRR7746570.fastq.gz	Fastqsanger.gz
	SRR7746580.fastq.gz	Fastqsanger.gz
	SRR7746590.fastq.gz	Fastqsanger.gz
	SRR7746600.fastq.gz	Fastqsanger.gz
	SRR7746610.fastq.gz	Fastqsanger.gz

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TempO-Seq S1500+ Platform Gene Expression of Rat Liver Mode of Action Samples

Project description:Using the TempO-Seq rat S1500+ platform we performed gene expression analysis of using 63 purified RNA samples from the livers of rats exposed to controls or chemicals that fall into one of five modes of action (MOAs): constitutive androstane receptor/pregnane X receptor (CAR/PXR) activation, aryl hydrocarbon receptor (AhR) activation, peroxisome proliferator-activated receptor-alpha (PPARA) activation, cytotoxicity or DNA damage. The TempO-Seq data generated was used to compare to gene expression data acquired from the same samples run on Affymetrix microarays (GEO: GSE47875) and Illumina RNA-Seq (GEO: GSE55347, SRA:SRP039021).

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Project description:The TempO-SeqTM platform allows for targeted transcriptomic analysis and is currently used by many groups to perform high-throughput gene expression analysis. Herein we performed a comparison of gene expression characteristics measured using 45 purified RNA samples from the livers of rats exposed to chemicals that fall into one of five modes of action (MOAs). These samples have been previously evaluated using AffymetrixTM rat genome 230 2.0 microarrays and Illumina® whole transcriptome RNA-Seq. Comparison of these data with TempO-Seq analysis using the rat S1500+ beta gene set identified clear differences in the platforms related to signal to noise, root mean squared error, and/or sources of variability. Microarray and TempO-Seq captured the most variability in terms of MOA and chemical treatment whereas RNA-Seq had higher noise and larger differences between samples within a MOA. However, analysis of the data by hierarchical clustering, gene subnetwork connectivity and biological process representation of MOA-varying genes revealed that the samples clearly grouped by treatment as opposed to gene expression platform. Overall these findings demonstrate that the results from the TempO-Seq platform are consistent with findings on other more established approaches for measuring the genome-wide transcriptome.

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