QC mix; quality control mix of 6 compounds trial2
Ontology highlight
ABSTRACT:
INSTRUMENT(S): impact II
ORGANISM(S): No Species
SUBMITTER: 
Chambers Hughes
PROVIDER: MSV000092587 | MassIVE | Thu Aug 03 09:45:00 BST 2023
REPOSITORIES: MassIVE
Similar Datasets
Project description:This is the standard QC 6-six mix as a test. This is just a learning tool.
2023-08-03 | MSV000092586 | MassIVE
Project description:This is another test of the quality control text mix. This is not really useful for anybody but me.
2023-08-04 | MSV000092593 | MassIVE
Project description:This is a test, this is only a test, of a 6 mix. This is not useful data. Please don't publish.
2023-08-04 | MSV000092594 | MassIVE
Project description:Glutaminyl cyclase (QC) activity in macrophage cells is correlated with the gene expression of MCP-2 and QC-catalyzed N-terminal pGlu formation of MCPs is required for macrophage migration and provide new insights into the role of QC in the inflammation process.
2021-10-22 | GSE21082 | GEO
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:Mitochondria sample replicate 1
2011-10-10 | PRD000373 | Pride
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: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:Lacticaseibacillus rhamnosus strain:QC Genome sequencing and assembly
| PRJNA641067 | ENA
Project description:Pisolithus sp mix smRNA - Indirect_FLM_Redo transcriptome
| PRJNA442580 | ENA