Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14874869_1.fastq.gz | Fastqsanger.gz | |||
| SRR14874869_2.fastq.gz | Fastqsanger.gz | |||
| SRR14874870_1.fastq.gz | Fastqsanger.gz | |||
| SRR14874870_2.fastq.gz | Fastqsanger.gz | |||
| SRR14874871_1.fastq.gz | Fastqsanger.gz |
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Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data. We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.. Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with TPM > 4). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples. We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.
2023-09-29 | GSE244325 | GEO
Project description:Microarray technology provides a powerful tool for defining gene expression profiles of airway epithelium that lend insight into the pathogenesis of human airway disorders. The focus of this study was to establish rigorous quality control parameters to ensure that microarray assessment of the airway epithelium is not confounded by experimental artifact. Samples (total n=223) of trachea, large and small airway epithelium were collected by fiberoptic bronchoscopy of 144 individuals (42 healthy non-smokers, 49 healthy smokers, 11 symptomatic smokers, 22 smokers with lone emphysema with normal spirometry, and 20 smokers with COPD) were processed and hybridized to Affymetrix HG-U133 2.0 Plus microarrays. The pre- and post-chip quality control (QC) criteria established, included: (1) RNA quality, assessed by RNA Integrity Number (RIN) ≥7.0 using Agilent 2100 Bioanalyzer software; (2) cRNA transcript integrity, assessed by signal intensity ratio of GAPDH 3' to 5' probe sets ≤3.0; and (3) the multi-chip normalization scaling factor ≤10.0 Of the 223 samples, 213 (95.5%) passed the QC criteria. In a data set of 34 arrays (10 samples failing QC criteria, 24 randomly chosen samples passing QC criteria), correlation coefficients for pairwise comparisons of expression levels for 100 housekeeping genes in which at least one array failed the QC criteria were significantly lower (average Pearson r = 0.90 ± 0.04) and more broadly dispersed than correlation coefficients for pairwise comparisons between any two arrays that passed the QC criteria (average Pearson r = 0.97 ± 0.01). By using the QC cutoff criteria, the inter-array variability, as assessed by the coefficient of variation in the expression levels for 100 housekeeping genes, was reduced from 35.7% to 21.7%. Based on the aberrant housekeeping gene data generated from samples failing the established QC criteria, we propose that the QC criteria outlined in this study can accurately distinguish high quality from low quality data and can be used to delete poor quality microarray samples before proceeding to higher-order biological analyses and interpretation.
2010-09-20 | GSE11906 | GEO
Project description:miRTrace is a tool for quality control and tracing taxonomic origins of microRNA sequencing data. It operates in two modes: Trace mode, in which the software reports the composition of clade-specific miRNAs; and QC mode, in which it performed an all-round quality control. To validate the QC mode of the software, we subjected the in-house control samples from HEK-293T cells to various treatments, such as cross-species contamiantion with Drosophila S2 RNAs, sample dilution and RNase A digestion. These samples were processed using QC mode of miRTrace. We demonstrate that miRTrace accurately identities poor-quality samples and to some extent even the causes of the compromised quality.
2018-10-31 | GSE118437 | GEO
Project description:The quiescent center (QC) plays an essential role during root development by creating a microenvironment that preserves the stem cell fate of its surrounding cells. Strikingly, in order to retain root structure, QC cells only occasionally self-renew, displaying a proliferation rate far below that of all other cells within the root meristem. Previously, the APC/CCCS52A2 ubiquitine ligase and brassinosteroid signaling pathways have been found to antagonistically control Arabidopsis thaliana QC cell proliferation. Here, we demonstrate that both pathways converge on the ERF115 transcription factor that acts as a rate-limiting factor of QC cell division through transcriptional control of the autocrine phytosulfokine PSK5 peptide hormone. ERF115 marks QC cell division but is restrained through proteolysis by the APC/CCCS52A2 ubiquitine ligase, whereas QC proliferation is driven by brassinosteroid-dependent ERF115 expression. Combined, these two antagonistic mechanisms delimit the ERF115-PSK5 activity and QC renewal. Our results reveal a unique cell cycle regulatory mechanism that accounts for the low proliferation rate of QC cells within a surrounding population of highly mitotic active cells.
2013-11-04 | GSE48793 | GEO
Project description:This dataset includes ChIP-seq data for H3K27ac and H3K4me1 on 20 paired samples of colorectal cancer and adjacent normal mucosa. One tumor sample that failed QC is not available.
| EGAD00001007707 | EGA
Project description:The Guthrie 903 card archived dried blood spots (DBS) are a unique but terminal resource amenable for individual and population wide genomic profiling. The limited amounts of DBS-derived genomic DNA (gDNA) can be whole-genome amplified (WGA) producing sufficient gDNA for genomic applications, albeit with variable success, and optimizing the isolation of high-quality DNA from these finite, low-yield specimens is essential. Visual automated fluorescence electrophoresis (VAFE) is a novel QC technology affording precise quality, quantity and molecular weight of double-stranded DNA from a single microliter of sample. The VAFE QC data were correlated with subsequent sample performance in PCR, sequencing, and high-density comparative genome hybridization array.
2013-07-01 | GSE44249 | GEO
Project description:Microarray technology provides a powerful tool for defining gene expression profiles of airway epithelium that lend insight into the pathogenesis of human airway disorders. The focus of this study was to establish rigorous quality control parameters to ensure that microarray assessment of the airway epithelium is not confounded by experimental artifact. Samples (total n=223) of trachea, large and small airway epithelium were collected by fiberoptic bronchoscopy of 144 individuals (42 healthy non-smokers, 49 healthy smokers, 11 symptomatic smokers, 22 smokers with lone emphysema with normal spirometry, and 20 smokers with COPD) were processed and hybridized to Affymetrix HG-U133 2.0 Plus microarrays. The pre- and post-chip quality control (QC) criteria established, included: (1) RNA quality, assessed by RNA Integrity Number (RIN) ≥7.0 using Agilent 2100 Bioanalyzer software; (2) cRNA transcript integrity, assessed by signal intensity ratio of GAPDH 3' to 5' probe sets ≤3.0; and (3) the multi-chip normalization scaling factor ≤10.0 Of the 223 samples, 213 (95.5%) passed the QC criteria. In a data set of 34 arrays (10 samples failing QC criteria, 24 randomly chosen samples passing QC criteria), correlation coefficients for pairwise comparisons of expression levels for 100 housekeeping genes in which at least one array failed the QC criteria were significantly lower (average Pearson r = 0.90 ± 0.04) and more broadly dispersed than correlation coefficients for pairwise comparisons between any two arrays that passed the QC criteria (average Pearson r = 0.97 ± 0.01). By using the QC cutoff criteria, the inter-array variability, as assessed by the coefficient of variation in the expression levels for 100 housekeeping genes, was reduced from 35.7% to 21.7%. Based on the aberrant housekeeping gene data generated from samples failing the established QC criteria, we propose that the QC criteria outlined in this study can accurately distinguish high quality from low quality data and can be used to delete poor quality microarray samples before proceeding to higher-order biological analyses and interpretation. Affymetrix arrays were used to assess the quality of gene expression data in trachea, large airway and small airway epithelium obtained by fiberoptic bronchoscopy of 42 healthy non-smokers, 49 healthy smokers, 11 symptomatic smokers, 22 smokers with lone emphysema with normal spirometry, and 20 smokers with COPD.
2010-09-19 | E-GEOD-11906 | biostudies-arrayexpress
Project description:The quiescent center (QC) plays an essential role during root development by creating a microenvironment that preserves the stem cell fate of its surrounding cells. Strikingly, in order to retain root structure, QC cells only occasionally self-renew, displaying a proliferation rate far below that of all other cells within the root meristem. Previously, the APC/CCCS52A2 ubiquitine ligase and brassinosteroid signaling pathways have been found to antagonistically control Arabidopsis thaliana QC cell proliferation. Here, we demonstrate that both pathways converge on the ERF115 transcription factor that acts as a rate-limiting factor of QC cell division through transcriptional control of the autocrine phytosulfokine PSK5 peptide hormone. ERF115 marks QC cell division but is restrained through proteolysis by the APC/CCCS52A2 ubiquitine ligase, whereas QC proliferation is driven by brassinosteroid-dependent ERF115 expression. Combined, these two antagonistic mechanisms delimit the ERF115-PSK5 activity and QC renewal. Our results reveal a unique cell cycle regulatory mechanism that accounts for the low proliferation rate of QC cells within a surrounding population of highly mitotic active cells. ChIP-seq analysis of genes bound by the ERF115 transcription factor, using mock ChIP with wild type cells as negative control. Analyzed by Illumina HiSeq
2013-11-04 | E-GEOD-48793 | biostudies-arrayexpress
Project description:The Arabidopsis quiescent center (QC) is a small group of cells with low mitotic activity located at the center of the root stem cell niche. Its transcriptional profile was previously analyzed using two repeats of cells FACS isolated using the WOX5 marker. To get more power in analyzing QC transcriptional profile, we generated three additional samples of the QC, using the QC-specific marker WOX5.
2014-12-17 | GSE64253 | GEO