Similar Datasets
Project description:Analysis of gene expression level. The hypothesis tested in the present study was that 5-azacytidine affects fruit ripening.
2022-11-15 | GSE164242 | GEO
Project description:Next Generation Sequencing Facilitates Quantitative Analysis of watermelon fruit Whole Genome BS-Seq
| PRJNA689616 | ENA
Project description:Analysis of small RNA expression level.
2022-11-15 | GSE164244 | GEO
Project description:Next Generation Sequencing Facilitates Quantitative Analysis of watermelon fruit Transcriptomes
| PRJNA689612 | ENA
Project description:Next Generation Sequencing Facilitates Quantitative Analysis of watermelon fruit small RNA expression
| PRJNA689613 | ENA
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of plants. The goals of this study are to compare omparatively evaluated both sequence variation and gene expression at the transcriptomic level between two species. Methods: Pooled total RNA of P. floridana flower buds and young fruits, in triplicate, using Illumina HiSeqTM 2000. The sequence reads remove reads with adaptors or unknown nucleotides larger than 5% and low-quality reads using . qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Sequencing the Physalis transcriptome revealed 147,118 unigenes. When aligned to the tomato genome, we estimated that around 30,121 genes were expressed in the Physalis floral-fruit transcriptome, and 10,498 orthologous gene pairs were identified between P. floridana and S. pimpinellifolium.with a fold change ≥1.5 and FER value <0.001, 0.68% of the unigenes in the Physalis floral-fruit transcriptome were developmentally regulated at the floral-fruit transition, and Altered expression of 15 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Conclusions: Our study represents the first detailed analysis of floral-fruit 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 organ or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
2018-12-29 | GSE92998 | GEO
Project description:Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived normal human kidney 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 The kidney tissue was immediately placed and stored in RNAlater® (Ambion), according to the manufacturer’s instruction. The tissue was manually microdissected under microscope in RNAlater® pool for glomerular and tubular compartment. Dissected tissue was homogenized and RNA was prepared using RNAeasy mini columns (Qiagen, Valencia, CA, US), according to the manufacturer’s instructions. RNA quality and quantity were determined using the Laboratory-on-Chip Total RNA PicoKit Agilent BioAnalyzer. Only samples without evidence of degradation were further used (RNA Integrity Number >6).
2014-08-06 | E-GEOD-60119 | biostudies-arrayexpress
Project description:PurposeNext-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.MethodsRetinal mRNA profiles of 21-day-old wild-type (WT) and neural retina leucine zipper knockout (Nrl(-/-)) 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 TaqMan and SYBR Green assays.ResultsUsing 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 (R(2)) 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.ConclusionsOur 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.
| S-EPMC3233386 | biostudies-literature
Project description:We used RNAseq to analyse the transcriptomes of cycling and quiescent livers from cTKO (Rblox/lox;p130lox/lox;p107?/?) and control mice. mRNA profiles of cycling and quiescent control and TKO liver tissue were generated by deep sequencing in duplicate
2014-07-12 | E-GEOD-43631 | biostudies-arrayexpress
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: Retinal mRNA profiles of 21-day-old wild-type (WT) and neural retina leucine zipper knockout (Nrl-/-) 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 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. Retinal mRNA profiles of 21-day old wild type (WT) and Nrl-/- mice were generated by deep sequencing, in triplicate, using Illumina GAIIx.
2011-10-25 | E-GEOD-33141 | biostudies-arrayexpress