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.

The critical role that spectral libraries play in capturing the metabolomics community knowledge

“…This allows the researcher to accept spectrum annotations with a controlled false discovery rate such that they can decide how many incorrect matches they are willing to include in their results. Although a few other methods to control false discovery rates in metabolomics have been introduced (Palmer et al, 2016;Alka et al, 2022), none are currently routinely used. Statistical control of MS/MS spectrum annotations is an important area of research to explore further and advance untargeted metabolomics into a highly scalable quantitative technique, and we anticipate that such tools will become routinely accessible in emerging MS/MS-based spectrum annotation software.…”

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

“…For DDA, Passatutto 20 uses re-rooted fragmentation trees, JUMPm 21 adds a small odd numbers of hydrogen atoms, and XY-Meta 22 combines original and randomly selected MS2 peaks. And recently reported for DIA, DIAMetAlyzer 16 , provides an FDR estimation employing Passatutto 20 but it does not support the IM separation. These methods rely on annotated spectra or a sample-specific metabolite database for FDR estimation.…”

“…Multiple sub-scores are then calculated per peak-group to assess coelution and identification. Software employing TDX include Skyline 14 , MetDIA 15 , and DIAMetAlyzer 16 . Another tool demonstrated for DIA using a different approach is DecoID 17 , where the MS2 deconvolution is achieved by mixing database spectra to match an experimentally acquired spectrum using least absolute shrinkage and selection operator (LASSO) regression.…”

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics

“…Compared with proteomics, error rate estimation (i.e., false discovery rate) is under development in metabolomics. Recently, DIAMetAlyzer 39 reported an automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics. More specifically for LC–IM–MS-based lipidomics, MS-DIAL reported the FDR of lipid annotations during RT and CCS matches using their validation set 22 .…”