Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA325812 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR3670980.fastq.gz | Fastqsanger.gz | |||
| SRR3670990.fastq.gz | Fastqsanger.gz | |||
| SRR3670981.fastq.gz | Fastqsanger.gz | |||
| SRR3670991.fastq.gz | Fastqsanger.gz | |||
| SRR3670982.fastq.gz | Fastqsanger.gz |
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Project description:RNA-sequencing has become the gold standard for whole-transcriptome gene expression quantification. Multiple algorithms have been developed to derive gene counts from sequencing reads. While a number of benchmarking studies have been conducted, the question remains how individual methods perform at accurately quantifying gene expression levels from RNA-sequencing reads. We performed an independent benchmarking study using RNA-sequencing data from the well established MAQCA and MAQCB reference samples. RNA-sequencing reads were processed using five popular workflows (Tophat-HTSeq, Tophat-Cufflinks, STAR-HTSeq, Kallisto and Salmon) and resulting gene expression measurements were compared to expression data generated by wet-lab validated qPCR assays for all protein coding genes. All methods showed high gene expression rank correlations with qPCR data. When comparing gene expression fold changes between MAQCA and MAQCB samples, about 85% of the genes showed consistent results between RNA-sequencing and qPCR data. Of note, each method revealed a small but specific set of genes with inconsistent expression measurements. A significant proportion of these method-specific inconsistent genes were reproducibly identified in independent datasets. These genes were typically smaller, had fewer exons and were lower expressed compared to genes with consistent expression measurements. We propose that careful validation is warranted when evaluating RNA-seq based expression profiles for this specific set of genes.
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Project description:Long non-coding RNA expression profiling in the NCI60 cancer cell line panel using high-throughput RT-qPCR
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Project description:In this study we present an experimental pipeline that takes into consideration sample collection, processing, enrichment, and the subsequent comparative analysis of circulating small ribonucleic acids using small RNA sequencing and RT-qPCR. Initially, a panel of miRNAs dysregulated in circulating blood from breast cancer patients compared to healthy women were identified using small RNA sequencing. MiR-320a was identified as the most dysregulated miRNA between the two female cohorts. Total RNA and enriched small RNA populations (<30 bp) isolated from peripheral blood from the same female cohort samples were then tested using a miR-320a RT-qPCR assay. When total RNA was analyzed with this miR-320a RT-qPCR assay, a 2.3-fold decrease in expression levels was observed between blood samples from healthy controls and breast cancer patients. However, upon enrichment for the small RNA population and subsequent analysis of miR-320a using RT-qPCR, its dysregulation in breast cancer patients was more pronounced with an 8.89-fold decrease in miR-320a expression.
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Project description:Arabidopsis thaliana has been used regularly as a model plant in gene expression studies on transcriptional reprogramming upon pathogen infection, such as that by Pseudomonas syringae pv. tomato DC3000 (Pst DC3000), or when subjected to stress hormone treatments including jasmonic acid (JA), salicylic acid (SA), and abscisic acid (ABA). RT-qPCR has been extensively employed to quantitate these gene expression changes. However, the accuracy of the quantitation is largely dependent on the stability of the expressions of reference genes used for normalization. Recently, RNA-seq has been widely used to mine stably expressed genes for use as references in RT-qPCR. However, the amplification step in RNA-seq creates an intrinsic bias against those genes with relatively low expression levels, and therefore does not provide an accurate quantification of all expressed genes. In this study, we employed mass spectrometry-based label-free quantification (LFQ) in proteomic analyses to identify those proteins with abundances unaffected by Pst DC3000 infection. We verified, using RT-qPCR, that the levels of their corresponding mRNAs were also unaffected by Pst DC3000 infection. In addition, using RT-qPCR, we verified that the mRNAs were stably expressed upon stress hormone treatments including JA, SA, and ABA. Results indicated that the candidate genes identified here had stable expressions upon these stresses and are suitable to be used as reference genes for RT-qPCR. Among the 18 candidate reference genes reported in this study, many of them had greater expression stability than the commonly used reference genes, such as ACT7, in previous studies. Here, besides proposing more appropriate reference genes for Arabidopsis expression studies, we also demonstrated the capacity of mass spectrometry-based LFQ to quantify protein abundance and the possibility to extend protein expression studies to the transcript level.
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Project description:Three HCT116 cell lines (HCT WT, HCT p21-/-, HCT p53-/-) were analyzed by RT-qPCR array analysis for chromatin modification enzymes to identify potential target genes, that depend on the cell cycle regulator p21 and that are indpendent of p53. HCT116 cell lines (HCT WT, HCT p21-/-, HCT p53-/-) were cultured at 37°C in a humidified atmosphere with 5% CO2. Cells were harvested at a confluency of ~60-80% and RNA was isolatd using QIAzol an Rneasy Kits from Qiagen. cDNA was produced from equal amounts of RNA using the RT² First Strand Ki from Qiagen and RT-qPCR analysis was performed following the manufacturer's instructions using the Epigenetic Chromatin Modification Enzymes RT² Profiler PCR Array from Qiagen. The CFX96TM Real-Time System and the C1000TM Thermal Cycler from Bio-Rad were used to perform the RT-PCR runs. The three different cell lines were analyzed in three biological triplicates. The mean expression Ct values of the target genes were normalized against the mean Ct value for GAPDH to calculate the Fold Change values. The Raw Data was processed using the RT² Profiler PCR Array Data Analysis version 3.5 software from SABiosciences.
2019-12-28 | GSE107664 | GEO
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2019-12-28 | GSE107665 | GEO
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