Constructing a mass measurement error surface to improve automatic annotations in liquid chromatography/mass
Nir Shahaf1, Pietro Franceschi, Panagiotis Arapitsas
1Fondazione Edmund Mach, IASMA Research and Innovation Centre, via E. Mach 1, 38010, San Michele all'Adige, Italy; Faculty of Agriculture of The Hebrew University of Jerusalem, Rehovot, 76100, Israel.
Rapid Communications in Mass Spectrometry : RCM
|October 8, 2013
Summary
This study introduces a novel method for assessing mass measurement accuracy in mass spectrometry (MS), crucial for reliable metabolite identification in high-throughput metabolomics. The developed model predicts mass errors based on peak conditions, improving data analysis reproducibility.
Area of Science:
- Analytical Chemistry
- Metabolomics
- Mass Spectrometry
Background:
- Accurate mass measurement is fundamental for metabolite annotation in mass spectrometry (MS).
- Previous studies have not comprehensively addressed the reproducibility of mass measurements across diverse analytes and variable conditions typical in high-throughput metabolomics.
Purpose of the Study:
- To develop and validate a method for automatically assessing mass measurement errors in MS data.
- To address the gap in understanding mass measurement reproducibility in high-throughput metabolomics.
Main Methods:
- Developed an automated method to extract mass measurement errors from large quadrupole time-of-flight (QTOF) MS datasets.
- Utilized a statistical, data-driven approach to build a predictive model for mass measurement error confidence intervals based on individual ion peak conditions.
- Ensured a fast, high-throughput processing capability for the developed method.
Main Results:
- The predictive model demonstrated reproducibility across external datasets acquired under similar, though not identical, conditions.
- The approach offers advantages over traditional fixed mass measurement error limits.
- The model accurately predicts confidence intervals for absolute mass measurement error based on individual peak characteristics.
Conclusions:
- The proposed approach enables a more rational application of MS technology by automatically evaluating absolute mass measurement error.
- This method is immediately applicable for integration into high-throughput peak annotation pipelines for database searching.
- Enhances the reliability of metabolite identification in large-scale metabolomics studies.
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