Metabolomics

Dataset Information

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DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics


ABSTRACT: The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.

INSTRUMENT(S): Liquid Chromatography MS - positive - reverse phase

SUBMITTER: Oliver Alka 

PROVIDER: MTBLS1108 | MetaboLights | 2022-09-05

REPOSITORIES: MetaboLights

Dataset's files

Source:
Action DRS
MTBLS1108 Other
FILES Other
a_MTBLS1108_LC-MS_positive_reverse-phase_metabolite_profiling.txt Txt
i_Investigation.txt Txt
m_MTBLS1108_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsv Tabular
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Publications

DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics.

Alka Oliver O   Shanthamoorthy Premy P   Witting Michael M   Kleigrewe Karin K   Kohlbacher Oliver O   Röst Hannes L HL  

Nature communications 20220315 1


The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chroma  ...[more]

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