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 WT and dKO round spermatids transcriptome profiling (RNA-seq) Methods: Adult WT and dKO round spermatids mRNA profiles mice were generated by deep sequencing, in dulplicate. 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 dKO round spermatids, 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. Adult wild type (WT) and dKO mouse round spermatids were generated by deep sequencing, in dulplicate, using Illumina GAIIx.

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.

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 WT and dKO round spermatids transcriptome profiling (RNA-seq) Methods: Adult WT and dKO round spermatids mRNA profiles mice were generated by deep sequencing, in dulplicate. 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 dKO round spermatids, 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: RNA editing by ADAR1 is essential for hematopoietic development. The goals of this study were firstly, to identify ADAR1-specific RNA-editing sites by indentifying A-to-I (G) mismatches in RNA-seq data compared to mm9 reference genome in wild type mice that were not edited or reduced in editing frequency in ADAR1E861A editing deficient mice. Secondly, to determine the transcriptional consequence of an absence of ADAR1-mediated A-to-I editing. Methods: Fetal liver mRNA profiles of embryonic day 12.5 wild-type (WT) and ADAR1 editing-deficient (ADAR1E861A) mice were generated by RNA sequencing, in triplicate (biological replicates), using Illumina HiSeq2000. The sequence reads that passed quality filters were analyzed at the transcript level with TopHat followed by Cufflinks. qRT–PCR validation was performed using SYBR Green assays. A-to-I (G) RNA editing sites were identified as previously described by Ramaswami G. et al., Nature Methods, 2012 using Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA). RNA editing sites were confirmed by Sanger sequencing. Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 14,484 transcripts in the fetal livers of WT and ADAR1E861A mice with BWA. RNA-seq data had a goodness of fit (R2) of >0.94 between biological triplicates per genotype. Approximately 4.4% of the transcripts showed differential expression between the WT and ADAR1E861A fetal liver, with a LogFC≥1.5 and p value <0.05. A profound upregulation of interferon stimulated genes were found to be massively upregulated (up to 11 logFC) in ADAR1E861A fetal liver compared to WT. 6,012 A-to-I RNA editing sites were identified when assessing mismatches in RNA-seq data of WT and ADAR1E861A fetal liver. Conclusions: Our study represents the first detailed analysis of fetal liver transcriptomes and A-to-I RNA editing sites, with biologic replicates, generated by RNA-seq technology. A-to-I RNA editing is the essential function of ADAR1 and is required to suppress interferon signaling to endogenous RNA. Fetal liver mRNA profiles of E12.5 wild type (WT) and ADAR E861A mutant mice were generated by deep sequencing, in triplicate, using Illumina HiSeq 200.

Project description:Purpose: RNA editing by ADAR1 is essential for hematopoietic development. The goals of this study were firstly, to identify ADAR1-specific RNA-editing sites by indentifying A-to-I (G) RNA editing sites in wild type mice that were not edited or reduced in editing frequency in ADAR1 deficient murine erythroid cells. Secondly, to determine the transcription consequence of an absence of ADAR1-mediated A-to-I editing. Methods: Total RNA from E14.5 fetal liver of embryos with an erythroid restricted deletion of ADAR1 (KO) and littermate controls (WT), in duplicate. cDNA libraries were prepared and RNA sequenced using Illumina HiSeq2000. The sequence reads that passed quality filters were analyzed at the transcript level with TopHat followed by Cufflinks. qRTâPCR validation was performed using SYBR Green assays. A-to-I (G) RNA editing sites were identified as previously described by Ramaswami G. et al., Nature Methods, 2012 using BurrowsâWheeler Aligner (BWA) followed by ANOVA (ANOVA). RNA editing sites were confirmed by Sanger sequencing. Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 14,484 transcripts in the fetal livers of WT and ADAR1E861A mice with BWA. RNA-seq data had a goodness of fit (R2) of >0.7, p<0.0001 between biological duplicates per genotype. Clusters of hyper-editing were onserved in long, unannotated 3'UTRs of erythroid specific transcripts. A profound upregulation of interferon stimulated genes were found to be massively upregulated (up to 5 log2FC) in KO fetal liver compared to WT. 11.332 (6,894 novel) A-to-I RNA editing sites were identified when assessing mismatches in RNA-seq data. Conclusions: Our study represents the first detailed analysis of erythroid transcriptomes and A-to-I RNA editing sites, with biologic replicates, generated by RNA-seq technology. A-to-I RNA editing is the essential function of ADAR1 and is required to prevent sensing of endogenous transcripts, likely via a RIG-I like receptor mediated axis. Fetal liver mRNA profiles of E14.5 wild type (WT) and ADAR Epor-Cre knock out mice were generated by deep sequencing, in duplicate using Illumina HiSeq 2000.

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RNA-seq approach to analysis of gene expression profiles between wild-type and C19orf12 knockdown A549 cell line

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