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Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and Acss2-/- liver Transcriptomes

ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and Acss2-/- liver Transcriptomes

PROVIDER: PRJNA485999 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR7692680.fastq.gz	Fastqsanger.gz
	SRR7692681.fastq.gz	Fastqsanger.gz
	SRR7692682.fastq.gz	Fastqsanger.gz
	SRR7692683.fastq.gz	Fastqsanger.gz
	SRR7692684.fastq.gz	Fastqsanger.gz

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Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and Acss2-/- liver Transcriptomes

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived liver transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis Methods: Liver mRNA profiles of 48 hours fasted wild-type (WT) and Acyl-CoA Synthetase Short Chain Family Member 2 knockout (Acss2−/−) mice were generated by deep sequencing, in triplicate, using Illumina GAIIx. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9). Conclusions: Our study represents the first detailed analysis of liver transcriptomes of Acss2-/- mice, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

2018-09-25 | GSE118552 | GEO

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