CSHL_Metabolomics_Beeromics_Project_MS1_and_MS2_Files
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive HF
ORGANISM(S): Not Applicable
SUBMITTER: 
Allegra Aron
PROVIDER: MSV000092247 | MassIVE | Thu Jun 22 20:56:00 BST 2023
REPOSITORIES: MassIVE
Similar Datasets
Project description:Beer samples were extracted by students participating in the course and a pooled QC sample was analyzed in positive and negative mode. This dataset contains only the positive and negative mode pooled QC samples
2023-06-22 | MSV000092245 | MassIVE
Project description:Beer samples were extracted by students participating in the course and a pooled QC sample was analyzed in positive and negative mode. This dataset contains the positive and negative mode pooled QC samples along with the MS1 files.
2023-06-22 | MSV000092247 | GNPS
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:Here we present MS1 spectra of nontoxigenic and toxigenic BF cells membrane extract, complemented by MS/MS spectra of separate parent ions in positive and negative mode
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MS1 total spectra BFplus and BFminus in negative and positive mode
2015-09-04-pos-bminus-baselinel-1.d - MS1 acquisition spectre of sample BFminus in pos mode, sample acquisition started from 7.5 min of chromatogramm
2015-09-04-pos-bminus-picture-tertac-dla lockmass.d - MS1 acquisition spectre of sample BFminus in pos mode, tetracycline as internal standard was used,m/z 445
NAT-neg-6-plus.d - MS1 acquisition spectre of sample BFplus in neg mode
nat-plus-pos.d- MS1 acquisition spectre of sample BFplus in pos mode
NAT-neg-2-minus-1-10.d - MS1 acquisition spectre of sample BFminus in neg mode
2016-12-07 | MSV000080382 | MassIVE
Project description:Transcriptional analysis of ovine skin response to infection with the parasitic mite Psoroptes ovis using the Agilent ovine transcriptome microarray platform. The results of statistical analysis (differential gene expression across the time course of infection was determined using a one way-analysis of variance (ANOVA) with a Student-Newman-Keuls (SNK) post-hoc test in Genespring GX 11.0 (Agilent Technologies, UK) comparing each of the 5 time points, non-infected and 1, 3, 6 and 24 hours post infection. Multiple test correction was performed using the Benjamini & Hochberg False Discovery Rate (FDR) procedure with an FDR corrected p-value cut-off set at 0.05) and fold change analysis (Fold change analysis was performed on the one-way anova dataset, probes with a fold change greater than 2 between any of the conditions were carried forward) are in archive E-TABM-1012.additional.zip. Quality control: Six biological replicates (sheep, n=6), multiple biopsies pooled from each animal at each time point of infection. Pooled samples from each sheep at each time point hybridised in single-dye (Cy3) format to Agilent ovine arrays. Positive and negative control probes utilised to assess array QC and real time qRT-PCR validation of selected array probes
2010-08-01 | E-TABM-1012 | biostudies-arrayexpress
Project description:We enrolled 37 SARS-CoV-2 positive patients including 17 short course (SC) COVID-19 patients (twice viral RNA detection negative over 24 hrs) and 20 COVID-19 long course (LC) COVID-19 patients. We also included 35 SARS-CoV-2 negative patients as control group. We collected 224 inactive serum samples from this cohort and their clinical data. For those Covid-19 patients, we included 37 patients with 2-9 weeks comparing with negative control patients and used tandem mass tag (TMT) based proteomics method to profile temporal proteomics change for this cohort.
2022-07-26 | PXD035471 | iProX
Project description:International guidelines recommend deciding the treatment of colorectal lesions based on the estimated histology by endoscopic optical diagnosis. However, the theoretical and practical knowledge on optical diagnosis is not widely expanded
The mail goal of this randomised controlled trial is to compare the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps > 20 mm assessed in routine colonoscopies of gastroenterologists attending a e-learning module (intervention group) vs gastroenterologists who do not (control group)
The main questions the study aims to answer are:
* Is the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps assessed in routine colonoscopies increased in those gastroenterologists participating in the e-learning module?
* Is the pooled diagnostic accuracy of optical diagnosis for predicting deep sm invasion in large non-pedunculated polyps ≥ 20 mm assessed in routine colonoscopies increased in those gastroenterologists participating in the e-learning module?
* In lesions with submucosal invasion, is the en bloc and complete resection rate (R0) increased in those gastroenterologists participating in the e-learning module?
* In lesions referred to surgery, is the pooled benign polyps rate decreased in those gastroenterologists participating in the e-learning module?
* In lesions treated with advanced en bloc procedures (ESD, TAMIS, fullthickness resection), is the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion increased in those gastroenterologists participating in the e-learning module?
* In lesions treated with piecemeal endoscopic resection, is the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion decreased in those gastroenterologists participating in the e-learning module?
* Is the diagnostic accuracy for predicting deep submucosal invasion in a test with pictures increased after participating in the e-learning module?
The participants (or subjects of study) are gastroenterologists. They will be randomised to do the e-learning course (intervention group) or not (control group).
Researchers will compare clinical outcomes of gastroenterologists participating in the e-learning module vs gastroenterologists not participating in the e-learning module to see if:
* the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps > 20 mm assessed in routine colonoscopies is increased.
* the pooled diagnostic accuracy of optical diagnosis for predicting deep sm invasion in large non-pedunculated polyps > 20 mm is increased.
* the en bloc and complete resection rate (R0) is increased in lesions with submucosal invasion.
* the pooled benign polyps rate decreased in lesions referred to surgery.
* the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion increased in lesions treated with advanced en bloc procedures (ESD, TAMIS, fullthickness resection).
* the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion decreased in lesions treated with piecemeal endoscopic resection.
* the diagnostic accuracy for predicting deep submucosal invasion in a test with pictures after participating is increased.
| 3397 | ecrin-mdr-crc
Project description:Dataset of six samples of pooled human urine, aliquoted, and independently prepared and aquired by NMR spectroscopy, and UPLC-MS. Each source sample is seperatly prepared and assayed thirteen times. A pooled QC sample and independent external reference of a compariable matrix is also aquired to assist in assesing analytical precision.
2018-08-31 | MTBLS694 | MetaboLights
Project description:Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.
2013-10-04 | PXD000324 | Pride