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MetabolomicsAutomated Label-free Quantification of Metabolites from Liquid Chromatography–Mass Spectrometry Data (Plasma)

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require autom...

2015-12-16 | MTBLS234 | MetaboLights
MetabolomicsAutomated Label-free Quantification of Metabolites from Liquid Chromatography–Mass Spectrometry Data (Simulated)

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require autom...

2015-12-16 | MTBLS235 | MetaboLights
ProteomicsMaxLFQ label-free quantification algorithm benchmark datasets
We developed a set of algorithms for label-free quantification, termed MaxLFQ, embedded into MaxQuant. This contains two datasets to benchmark MaxLFQ: The proteome benchmark dataset consists of of HeLa and E. coli lysates mixed at defined ratios. The dynamic range benchmark dataset consists of UPS1...
ORGANISM(S): Escherichia Coli (ncbitaxon:562) Homo Sapiens (ncbitaxon:9606) 
2017-12-19 | MSV000081831 | MassIVE
ProteomicsLFQbench enables a multi-centered benchmark study demonstrating robust proteomic label-free quantification
The consistent and accurate quantification of proteins is a challenging task for mass spectrometry (MS)-based proteomics. SWATH-MS uses data-independent acquisition (DIA) for label-free quantification. Here we evaluated five software tools for processing SWATH-MS data: OpenSWATH, SWATH2.0, Skyline, ...
ORGANISM(S): Escherichia Coli (ncbitaxon:562) Homo Sapiens (ncbitaxon:9606) Saccharomyces Cerevisiae (ncbitaxon:4932) 
2017-04-27 | MSV000081024 | MassIVE
MetabolomicsA combined direct infusion/reversed phase chromatography – high resolution mass spectrometry workflow for accurate absolute lipid quantification with 13C- internal standards
We propose a fully automated novel workflow for lipidomics based on flow injection, followed by liquid chromatography-high-resolution mass spectrometry (FI/LC-HRMS). The workflow combined in-depth characterization of the lipidome achieved via reversed-phase LC-HRMS with absolute quantification by us...
2021-07-01 | MTBLS1876 | MetaboLights
ProteomicsCombining label-free and label-based accurate quantifications with SWATH-MS: comparison with SRM and PRM for the evaluation of bovine muscle type effects.
Mass spectrometry has proven to be a valuable tool for the accurate quantification of proteins. In this study, we have evaluated the performances of three targeted approaches, namely Selected Reaction Monitoring (SRM), Parallel Reaction Monitoring (PRM) and Sequential Windowed Acquisition of Theoret...
ORGANISM(S): Bos Taurus 
2021-06-08 | PXD020680 | panorama
ProteomicsMaxLFQ label-free quantification algorithm benchmark datasets
We developed a set of algorithms for label-free quantification, termed MaxLFQ, embedded into MaxQuant. This contains two datasets to benchmark MaxLFQ: The proteome benchmark dataset consists of of HeLa and E. coli lysates mixed at defined ratios. The dynamic range benchmark dataset consists of UPS1...
ORGANISM(S): Homo sapiens (Human) Escherichia coli 
2014-09-17 | PXD000279 | Pride
Benchmark datasets Label-free quantification Maxlfq Maxquant 
ProteomicsLabel-Free Absolute Protein Quantification with Data-Independent Acquisition
Label-free absolute quantitative proteomics is commonly used for absolute quantification of the proteome or specific proteins of interest in various biological samples. Current label-free absolute protein quantification (APQ) methods determine MS1 intensities, MS2 spectral counts or intensities to a...
ORGANISM(S): Homo sapiens (Human) 
2019-03-20 | PXD010912 | Pride
Human Dia S9 Absolute protein quantification Microsome Liver Dda 
ProteomicsSurfactant-assisted one-pot sample preparation for label-free single-cell proteomics
Large numbers of cells are generally required for quantitative global proteome profiling due to the significant surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible...
ORGANISM(S): Homo Sapiens 
2021-03-11 | PXD022827 | panorama
ProteomicsLFQbench enables a multi-centered benchmark study demonstrating robust proteomic label-free quantification
The consistent and accurate quantification of proteins is a challenging task for mass spectrometry (MS)-based proteomics. SWATH-MS uses data-independent acquisition (DIA) for label-free quantification. Here we evaluated five software tools for processing SWATH-MS data: OpenSWATH, SWATH2.0, Skyline, ...
ORGANISM(S): Homo sapiens (Human) Escherichia coli Saccharomyces cerevisiae (Baker's yeast) 
2016-09-27 | PXD002952 | Pride
Lfqbench Lfq Swath-ms Benchmark 
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