Literature DB >> 28752757

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

Owen D Myers1, Susan J Sumner2, Shuzhao Li3, Stephen Barnes4, Xiuxia Du1.   

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.

Entities:  

Mesh:

Year:  2017        PMID: 28752757     DOI: 10.1021/acs.analchem.7b01069

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  30 in total

1.  Metabolomic "Dark Matter" Dependent on Peroxisomal β-Oxidation in Caenorhabditis elegans.

Authors:  Alexander B Artyukhin; Ying K Zhang; Allison E Akagi; Oishika Panda; Paul W Sternberg; Frank C Schroeder
Journal:  J Am Chem Soc       Date:  2018-02-16       Impact factor: 15.419

2.  Metabolomics by UHPLC-MS: benefits provided by complementary use of Q-TOF and QQQ for pathway profiling.

Authors:  Katrin Freiburghaus; Carlo Rodolfo Largiadèr; Christoph Stettler; Georg Martin Fiedler; Lia Bally; Cédric Bovet
Journal:  Metabolomics       Date:  2019-08-28       Impact factor: 4.290

3.  Perspectives on Data Analysis in Metabolomics: Points of Agreement and Disagreement from the 2018 ASMS Fall Workshop.

Authors:  Erin S Baker; Gary J Patti
Journal:  J Am Soc Mass Spectrom       Date:  2019-08-22       Impact factor: 3.109

4.  Peak Annotation and Verification Engine for Untargeted LC-MS Metabolomics.

Authors:  Lin Wang; Xi Xing; Li Chen; Lifeng Yang; Xiaoyang Su; Herschel Rabitz; Wenyun Lu; Joshua D Rabinowitz
Journal:  Anal Chem       Date:  2019-01-10       Impact factor: 6.986

5.  Optimization of XCMS parameters for LC-MS metabolomics: an assessment of automated versus manual tuning and its effect on the final results.

Authors:  Oihane E Albóniga; Oskar González; Rosa M Alonso; Yun Xu; Royston Goodacre
Journal:  Metabolomics       Date:  2020-01-10       Impact factor: 4.290

6.  Deep Neural Networks for Classification of LC-MS Spectral Peaks.

Authors:  Edward D Kantz; Saumya Tiwari; Jeramie D Watrous; Susan Cheng; Mohit Jain
Journal:  Anal Chem       Date:  2019-09-19       Impact factor: 6.986

Review 7.  Decoding the Metabolome and Lipidome of Child Malnutrition by Mass Spectrometric Techniques: Present Status and Future Perspectives.

Authors:  Iqbal Mahmud; Mamun Kabir; Rashidul Haque; Timothy J Garrett
Journal:  Anal Chem       Date:  2019-11-14       Impact factor: 6.986

8.  ADAP-GC 4.0: Application of Clustering-Assisted Multivariate Curve Resolution to Spectral Deconvolution of Gas Chromatography-Mass Spectrometry Metabolomics Data.

Authors:  Aleksandr Smirnov; Yunping Qiu; Wei Jia; Douglas I Walker; Dean P Jones; Xiuxia Du
Journal:  Anal Chem       Date:  2019-07-05       Impact factor: 6.986

9.  MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.

Authors:  Kelsey Chetnik; Lauren Petrick; Gaurav Pandey
Journal:  Metabolomics       Date:  2020-10-21       Impact factor: 4.290

Review 10.  Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases.

Authors:  Sofia Khan; Gulayse Ince-Dunn; Anu Suomalainen; Laura L Elo
Journal:  J Clin Invest       Date:  2020-01-02       Impact factor: 14.808

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