» Articles » PMID: 28752757

Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data

Overview
Journal Anal Chem
Specialty Chemistry
Date 2017 Jul 29
PMID 28752757
Citations 48
Authors
Affiliations
Soon will be listed here.
Abstract

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS centWave peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2's use of centWave. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development.

Citing Articles

A fast region of interest algorithm for efficient data compression and improved peak detection in high-resolution mass spectrometry.

Munk Kronik O, Christensen J, Nielsen N, Tisler S, Tomasi G Anal Bioanal Chem. 2025; .

PMID: 39786495 DOI: 10.1007/s00216-024-05718-7.


Techniques, Databases and Software Used for Studying Polar Metabolites and Lipids of Gastrointestinal Parasites.

Wangchuk P, Yeshi K Animals (Basel). 2024; 14(18).

PMID: 39335259 PMC: 11428429. DOI: 10.3390/ani14182671.


A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry.

Sadia M, Boudguiyer Y, Helmus R, Seijo M, Praetorius A, Samanipour S Anal Bioanal Chem. 2024; .

PMID: 38995405 DOI: 10.1007/s00216-024-05425-3.


MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

Pang Z, Lu Y, Zhou G, Hui F, Xu L, Viau C Nucleic Acids Res. 2024; 52(W1):W398-W406.

PMID: 38587201 PMC: 11223798. DOI: 10.1093/nar/gkae253.


Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics.

Kumler W, Hazelton B, Ingalls A BMC Bioinformatics. 2023; 24(1):404.

PMID: 37891484 PMC: 10612323. DOI: 10.1186/s12859-023-05533-4.