Dataset Information

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

ABSTRACT:

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 automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems.

Plasma data is reported in the current study MTBLS234.

Simulated data is reported in MTBLS235.

OTHER RELATED OMICS DATASETS IN: PXD006204

INSTRUMENT(S): SYNAPT HDMS (Waters)

SUBMITTER: Erhan Kenar

PROVIDER: MTBLS234 | MetaboLights | 2015-12-16

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MTBLS234	Other
	FILES	Other
	a_MTBLS234_metabolite_quantitation_benchmark_metabolite_profiling_mass_spectrometry.txt	Txt
	i_Investigation.txt	Txt
	s_MTBLS234.txt	Txt

Items per page:

1 - 5 of 5

Publications

Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data.

Kenar Erhan E Franken Holger H Forcisi Sara S Wörmann Kilian K Häring Hans-Ulrich HU Lehmann Rainer R Schmitt-Kopplin Philippe P Zell Andreas A Kohlbacher Oliver O

Molecular & cellular proteomics : MCP 20131031 1

PMID: 24176773

Similar Datasets

Project description: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 automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems. Simulated data is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS235' rel='noopener noreferrer' target='_blank'>MTBLS235</a>.Plasma data is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS234' rel='noopener noreferrer' target='_blank'>MTBLS234</a>.