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.
Highlights
The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount
The workflow takes advantage of an experiment-specific assay library curated based on available dependent acquisition (DDA) data and is tailored to a specific question and instrument
The resulting target-decoy assay library allows for the targeted extraction and scoring of targeted transitions from the Data-independent acquisition (DIA) data with false-discovery rates (FDRs) control
Summary
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. These XICs (one for each fragment ion) have to be verified for quality and compared with an internal (spikedin) or external standard, which is currently a laborious and manual task that requires specialized expertize and training While both the creation of assay libraries for DIA analysis and the processing of XICs has been automated in other fields[5], this is not the case in metabolomics. A combination of semi-supervised machine learning and on-the-fly decoy generation permits the estimation of statistically well-calibrated FDRs for the resulting data sets
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