GNPS MassIVE data submission of Artemisia MSe data
Ontology highlight
ABSTRACT: LC-MSe data of 5 Artemisia species (whole plant body) extracts for analyzing their chemical diversity, including 7 QC samples (mixture of all the samples in same conc.)
Project description:LC-MSe data of 13 Taraxacum species (whole plant body) extracts for analyzing their chemical diversity, including 8 QC samples (mixture of all the samples in same conc.)
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.
Project description:LC-MS/MS data used in a work titled "Assessing Specialized Metabolite Diversity of Alnus species by Digitized LC_MS/MS Data Analysis Workflow". Negative ion mode DDA data from 15 extracts of 4 Alnus species (barks, twigs, leaves, and fruits)
Project description:Every laboratory performing mass spectrometry based proteomics strives to generate high quality data. Among the many factors that influence the outcome of any experiment in proteomics is performance of the LC-MS system, which should be monitored continuously. This process is termed quality control (QC). We present an easy to use, rapid tool, which produces a visual, HTML based report that includes the key parameters needed to monitor LC-MS system perfromance. The tool, named RawBeans, can generate a report for individual files, or for a set of samples from a whole experiment. We anticipate it will help proteomics users and experts evaluate raw data quality, independent of data processing. The tool is available here: https://bitbucket.org/incpm/prot-qc/downloads.
Project description:The Project aims to provide resources for QC of multiple types of omic technologies and effective integration of diverse datasets from various scenarios. Large quantities of multi-omics materials, datasets, and best practice for their QC utilities were developed for whole process QC of large scale, multi-center, and longitudinal multi-omics profiling.
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 AGL42 marker. To get more power in analyzing QC transcriptional profile, we generated three additional samples of the QC, using the QC-specific marker WOX5. Three replicates of FACS-sorted GFP-positive cells from WOX5:GFP roots.
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.
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.
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.
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.