GNPS Data set of Amazonian Fungi 3 - Brazil
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive
ORGANISM(S): Fungi (ncbitaxon:4751)
SUBMITTER: 
Hector Koolen
PROVIDER: MSV000087789 | GNPS | Fri Jul 09 11:37:00 BST 2021
REPOSITORIES: GNPS
Similar Datasets
Project description:A set of LCMS data acquired in a micrOToF instrument (Bruker)
2021-07-09 | MSV000087788 | GNPS
Project description:A single run with a QEXACTIVE instrument for a new Penicillium species.
2021-07-12 | MSV000087797 | GNPS
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.
2013-10-03 | PXD000321 | Pride
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 | PXD000322 | Pride
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
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 | PXD000323 | Pride
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.
2013-10-03 | PXD000320 | Pride
Project description:In this study, we compared the proteomes of mouse CD4+Foxp3+ regulatory T cells (Treg) and CD4+Foxp3- conventional T cells (Tconv) in order to build a data set of proteins differentially regulated in these two cell populations. The data set contains mass spectrometry results from the analysis of 7 biological replicates of Treg/Tconv cell samples purified by flow cytometry, each experiment performed from a pool of 4-5 mice. Global proteomic analysis of each sample was performed by single-run nanoLC-MS/MS, using chromatographic separation of peptides on 50cm C18 reverse-phase columns, with either a 480min gradient on LTQ-Velos orbitrap mass spectrometer (replicates 1 and 2) or a 300min gradient on Q-Exactive orbitrap mass spectrometer (replicates 3-7). Several MS injection replicates were performed for some experiments, leading to 27 raw files composing the data set. The detailed description of each analysis (file name, sample type, biological replicate number, MS technical replicate number, MS instrument used, sample name in MaxQuant ouput) is given in the table “Files list.txt”.
2017-04-10 | PXD004436 | Pride
Project description:To study the function of Physcomitrella DCL4, a knockout plant line was created. Small RNAs from ~10-day old protonemata were obtained and sequenced using a SOLiD 2 instrument. A single library each was made from dcl4 mutant protonemata and wild-type protonemata. The dcl4 library was sequenced in four separate technical replicates. The wild-type sample was subject to a single sequencing run.
2012-04-01 | E-GEOD-18466 | biostudies-arrayexpress
Project description:we developed a web-based application (QCMAP) for interactive diagnosis and prediction of the per-formance of LC-MS systems across different biological sample types. Leveraging on a standardized HeLa sample run in Sydney MS core facility, we trained predictive models on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC-MS systems. Next, we demonstrated that the learned model can be applied to predict LC-MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web-application, QCMAP allows users to supply their own samples to benchmark the performance of their LC-MS systems and identify key factors for instrument opti-misation.. To demonstrate this, we obtained 10 datasets generated on a QECl instrument from mouse brain samples with different levels of quality.
2019-05-06 | PXD010307 | Pride