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Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and OsAFB6 overexpression mutant Transcriptomes

ABSTRACT: Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare transgene-derived OsAFB6 overexpression mutant transcriptome profiling (RNA-seq) to microarray Methods: young panicle(<1mm) mRNA profiles of 80 individual plants of OsAFB6 overexpression mutants and the correspondinng negative plants during the same transgenic event as control were generated by deep sequencing, in two replicate, using Illumina HiSeq 2000. 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. Results: Using an optimized data analysis workflow, we found 59 genes were upregulated and 229 genes were down regulated in OsAFB6 overexpression mutant. Conclusions: Our study represents the first detailed analysis of young panicle transcriptomes of OsAFB6 overexpression mutant, 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. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

ORGANISM(S): Oryza sativa

PROVIDER: GSE106954 | GEO | 2017/11/16

REPOSITORIES: GEO

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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 retinal 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: Arabidopsis mRNA profiles of 9-day-old wild-type (WT) and nid1 knockout mutant plants 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 TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to Arabidopsis sample and identified 16,014 transcripts in the 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 nid1 transcripts, 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 transcripts characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

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Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and OsAFB6 overexpression mutant Transcriptomes

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