Graphene oxide nanoparticles induce hepatic dysfunction through the regulation of innate immune signaling in zebrafish (Danio rerio)
Ontology highlight
ABSTRACT:
ORGANISM(S): Danio rerio
PROVIDER: GSE145760 | GEO | 2020/02/24
REPOSITORIES: GEO
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Project description:Purpose: Next-generation sequencing (NGS) has revolutionized system-based cell pathway analysis. The purposes of this study were to compare and analyze the expression differences of skin squamous cell line A431 in stem cells after Mir-22 deletion. Methods: mRNA profiles of control and case cells were generated by deep sequencing, in duplicate, using Illumina NovaSeq 6000. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods:Bowtie2 to map clean reads to reference gene and use HISAT to reference genome.Bowtie2 parameters forSE reads:-q--phred64--sensitive--dpad0--gbar and HISTAparameters forSE reads:-p8--phred64--sensitive-I1-X 1000. qRT–PCR validation was performed using SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 24 million sequence reads per sample to the human genome (build hg19) and identified 17000 transcripts in control and case cells with RSEM workflow. Approximately 5% of the transcripts showed differential expression between control and case cells, with a fold change ≥1.5 and p value <0.01. Altered expression of 10 candidate genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method.
2020-08-15 | GSE156255 | GEO
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to access grafted human neural stem cells and host tissue transcriptomes Methods: Grafted mRNA profiles of 7 days transplanted hNSC were generated by deep sequencing, in triplicate, using Illumina HiSeq 2500. The sequence reads that passed quality filters were analyzed at the transcript isoform level with Burrows–Wheeler Aligner (BWA) followed by RNA-Seq by Expectation Maximization (RSEM). qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, we mapped: 1) about 230 million sequence reads per sample to the human genome (build hg19) and identified 17,463 transcripts in the grafted hNSC; and 2) about 30 million sequence reads per sample to the rat genome (build rn5) and identified 13,4688 transcripts in the host tissues; using RSEM workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 4 housekeeping genes were validated with qPCR. RNA-seq data had a linear relationship with qPCR for more than four orders of magnitude. Approximately 14% of the transcripts showed differential expression between the grafted hNSC in naive and stroke; and approximately 41% of the transcripts showed differential expression between the naive and stroke tissues, with p value <0.05. Altered expression of over 10 genes was confirmed with qPCR, 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 grafted hNSC and host function. Data analysis with TRAP and RSEM workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of grafted hNSC transcriptome, with biologic replicates, generated by TRAPseq technology. TRAPseq and RSEM could be applied to many xenograft cell transplantation paradigms. With this approach we can start to predict upstream regulators that signal between the host and graft, and to predict the downstream signaling pathways and biological processes these upstream regulators might affect.
2023-02-08 | GSE150710 | GEO
Project description:Purpose: the goals of this study are to compare fruit of two clitivars oriental melon transcriptome profiling (RNA-seq) at different stages to explore carotenoid potentail carotenoid accumulation mechanism Methods:The transcriptome sequence of two cultivars oriental melon fruits at different stages were generated by deep sequencing with three repeats using Illumina. The sequence reads that passed filters were mapped to melon genome (http://cucurbitgenomics.org/organism/18) using HISAT2 software. The differently expressed genes were identify by |log2(FoldChange)| > 0 & padj <= 0.05, and qRT–PCR validation was performed using SYBR Green assays Result:Using an optimized data analysis workflow, we mapped about 40 million sequence reads per sample to the melon genome. The differentially expressed genes were functionally classified by GO and KEGG enrichment. We focused on carotenoid metabolism related gene and validated using qRT-PCR. The results showed RNA-seq and qRT-PCR were highly correlated. Conclusion: Our study provided transcriptome sequence of oriental melon fruits at different stages in two cultivars. The optimized data analysis workflows reported here should provide comparative framework of expression profiles. Our transcriptome characterization contribute to analyze gene functions and metabolic process of oriental melon.
2022-01-13 | GSE193412 | GEO
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.
2021-07-27 | GSE151501 | GEO
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: Liver tissues‘ mRNA profiles of 8-week-old wild-type (WT) and liver specific β-cateninΔ(ex3)/+ 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, Genes differential expression analysis between the WT and β-cateninΔ(ex3)/+ was performed by DESeq2 software between two different groups (and by edgeR between two samples). The genes with the parameter of false discovery rate (FDR) below 0.05 and absolute fold change ≥ 2 were considered differentially expressed genes. Differentially expressed genes were then subjected to enrichment analysis of GO functions and KEGG pathways. Conclusions: Our study represents the first detailed analysis of liver transcriptomes, with 3 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.
2024-01-01 | GSE188957 | GEO
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.
2014-07-03 | E-GEOD-59017 | biostudies-arrayexpress
Project description:Purpose: Probe the transcriptome-wide changes in the expression pattern between WT and Sertoli-specific Upf2 KO testes Methods: Total RNA were extracted from WT and Sertoli-specific Upf2 KO testes in triplicates and subject to deep-sequencing in Ion Torrent seq platform. 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 Upf2-mediated NMD pathway in Sertoli cell development Testis mRNA profiling was generated from postnatal day 4 WT and Amh-cKO (Sertoli specific Upf2 KO) testes, in triplicates.
2014-09-06 | E-GEOD-58405 | 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
Project description:Purpose: The goals of this study are to obtain the NGS-derived transcriptome profiling (RNA-seq) for THP-1 macrophages response to early secreted antigenic target 6-KDa (ESAT6) from Mycobacterium tuberculosis Methods: mRNA profiles of THP-1 macrophages treated with ESAT6 were generated by deep sequencing, using Illumina Hiseq3000. The sequence reads that passed quality filters were first mapped to the latest UCSC transcript set using Bowtie2 (version 2.1.0). Then the gene expression level was estimated using RSEM (RNA-Seq by Expectation Maximization, v1.2.15), and normalized with TMM (trimmed mean of M-values) to identify differentially expressed genes (DEGs) using the edgeR package edgeR. qRT–PCR validation was performed using SYBR Green assays. Results: Using an optimized data analysis workflow, we mapped about 23 million sequence reads per sample to the human genome (GRCh38/hg38) and identified 25,343 transcripts. Conclusions: Our study represents the first detailed analysis of transcriptomes for macrophages response to ESAT6, generated by RNA-seq technology.
2019-03-10 | GSE107387 | GEO
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 thyroid cancer cell line k1 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: mRNA profiles of shCtrl and shTBX3 cells were generated by deep sequencing, in duplicate, using BGISEQ-500. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods:Bowtie2 to map clean reads to reference gene and use HISAT to reference genome.Bowtie2 parameters forSE reads:-q--phred64--sensitive--dpad0--gbar and HISTAparameters forSE reads:-p8--phred64--sensitive-I1-X 1000. qRT–PCR validation was performed using SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 24 million sequence reads per sample to the human genome (build hg19) and identified 20511 transcripts in K1-shCtrl and K1-shTBX3 cells with RSEM workflow. Approximately 5% of the transcripts showed differential expression between K1-shCtrl and K1-shTBX3 cells, with a fold change ≥1.5 and p value <0.01. Altered expression of 16 candidate 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 thyroid cancer function. Conclusions: Our study represents the first detailed analysis of thyroid cancer cell line k1 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.
2020-10-30 | GSE106306 | GEO