Dataset Information

Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and CTCF cKO Transcriptomes

ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and CTCF cKO Transcriptomes

PROVIDER: PRJNA636090 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR11881245_1.fastq.gz	Fastqsanger.gz
	SRR11881245_2.fastq.gz	Fastqsanger.gz
	SRR11881246_1.fastq.gz	Fastqsanger.gz
	SRR11881246_2.fastq.gz	Fastqsanger.gz
	SRR11881247_1.fastq.gz	Fastqsanger.gz

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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 8-week-old wild-type (WT) and liver specific conditional CTCF KO (CTCF cKO) mice were generated by deep sequencing, in quadruplet, 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 TaqMan and 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) and identified 16,014 transcripts in the retinas of WT and Nrl−/− mice with BWA workflow and 34,115 transcripts with TopHat workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and Nrl−/− retina, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, 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.

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare small non-coding RNA profiling (snRNA-seq) in WT oocyte, sperm and 2PN stage embryos to those sperm and 2PN stage embryos derived from WT, Dicer cKO and Drosha cKO. We further study the roles of sperm-borne small RNA on fertilization and pre-implantation embryonic development. Methods: Small RNA profiles of adult wild-type (WT) oocytes, adult WT sperm, 2PN stage embryos, adult Dicer cKO/Drosha cKO sperm, 2PN stage embryos were generated by deep sequencing in duplicate, using Ion Torrent Proton. The sequence reads that passed quality filters were analyzed at the small RNA level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 small RNA (miRNA and endo-siRNA) in the oocyte, sperm and 2PN stage of WT and Dicer cKO/Drosha cKO mice with BWA workflow and 34,115 transcripts with TopHat workflow. Approximately 47% of the miRNAs showed differential expression between the WT and Dicer cKO sperm, ~52% of miRNAs were shown dysregulated in Drosha cKO sperm compared to those in WT sperm with a fold change ≥2.0 and p value <0.05. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of small non-coding RNAs (miRNAs) in sperm and demonstrated that sperm-borne small RNAs are important for fertilization and early embrynic develoment, with biologic duplicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of small RNAs profiles in mouse sperm, oocytes and 2PN stage of embryos. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of small RNA contents within sperm or oocytes/embryos. We conclude that RNA-seq based small RNAs characterization in gametes would expedite genetic network analyses and permit the dissection of complex biologic functions during fertilization and embryonic development.

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Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and CTCF cKO Transcriptomes

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