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-disc

SUBMITTER: Alka O 

PROVIDER: S-EPMC8924252 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature