MetMatch: A Semi-Automated Software Tool for the Comparison and Alignment of LC-HRMS Data from Different Metabolomics Experiments

Due to its unsurpassed sensitivity and selectivity, LC-HRMS is one of the major analytical techniques in metabolomics research. However, limited stability of experimental and instrument parameters may cause shifts and drifts of retention time and mass accuracy or the formation of different ion species, thus complicating conclusive interpretation of the raw data, especially when generated in different analytical batches. Here, a novel software tool for the semi-automated alignment of different measurement sequences is presented. The tool is implemented in the Java programming language, it features an intuitive user interface and its main goal is to facilitate the comparison of data obtained from different metabolomics experiments. Based on a feature list (i.e., processed LC-HRMS chromatograms with mass-to-charge ratio (m/z) values and retention times) that serves as a reference, the tool recognizes both m/z and retention time shifts of single or multiple analytical datafiles/batches of interest. MetMatch is also designed to account for differently formed ion species of detected metabolites. Corresponding ions and metabolites are matched and chromatographic peak areas, m/z values and retention times are combined into a single data matrix. The convenient user interface allows for easy manipulation of processing results and graphical illustration of the raw data as well as the automatically matched ions and metabolites. The software tool is exemplified with LC-HRMS data from untargeted metabolomics experiments investigating phenylalanine-derived metabolites in wheat and T-2 toxin/HT-2 toxin detoxification products in barley.

[1]  J. Carstensen,et al.  Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping , 1998 .

[2]  Mathieu Fourment,et al.  A comparison of common programming languages used in bioinformatics , 2008, BMC Bioinformatics.

[3]  A. Smilde,et al.  Inversion of peak elution order prevents uniform time alignment of complex liquid-chromatography coupled to mass spectrometry datasets. , 2014, Journal of chromatography. A.

[4]  Age K Smilde,et al.  Time alignment algorithms based on selected mass traces for complex LC-MS data. , 2010, Journal of proteome research.

[5]  P. A. Taylor,et al.  Synchronization of batch trajectories using dynamic time warping , 1998 .

[6]  R. Krska,et al.  GC–MS based targeted metabolic profiling identifies changes in the wheat metabolome following deoxynivalenol treatment , 2014, Metabolomics.

[7]  Bernhard Kluger,et al.  MetExtract: a new software tool for the automated comprehensive extraction of metabolite-derived LC/MS signals in metabolomics research , 2012, Bioinform..

[8]  E. Marcotte,et al.  Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. , 2006, Analytical chemistry.

[9]  F. Berthiller,et al.  Tracing the metabolism of HT-2 toxin and T-2 toxin in barley by isotope-assisted untargeted screening and quantitative LC-HRMS analysis , 2015, Analytical and Bioanalytical Chemistry.

[10]  B. Hammock,et al.  Mass spectrometry-based metabolomics. , 2007, Mass spectrometry reviews.

[11]  G. Thallinger,et al.  Untargeted Profiling of Tracer-Derived Metabolites Using Stable Isotopic Labeling and Fast Polarity-Switching LC–ESI-HRMS , 2014, Analytical chemistry.

[12]  Dan Ventura,et al.  LC-MS alignment in theory and practice: a comprehensive algorithmic review , 2013, Briefings Bioinform..

[13]  M. C. Cera,et al.  Algorithm 1 , 2014 .

[14]  Pan Du,et al.  Bioinformatics Original Paper Improved Peak Detection in Mass Spectrum by Incorporating Continuous Wavelet Transform-based Pattern Matching , 2022 .

[15]  P. Eilers Parametric time warping. , 2004, Analytical chemistry.

[16]  R. Abagyan,et al.  XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. , 2006, Analytical chemistry.

[17]  Age K. Smilde,et al.  Optimized time alignment algorithm for LC-MS data: correlation optimized warping using component detection algorithm-selected mass chromatograms. , 2008, Analytical chemistry.

[18]  O. Fiehn Metabolomics – the link between genotypes and phenotypes , 2004, Plant Molecular Biology.

[19]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[20]  Knut Reinert,et al.  OpenMS and TOPP: open source software for LC-MS data analysis. , 2011, Methods in molecular biology.

[21]  Xiang Zhang,et al.  Data pre-processing in liquid chromatography-mass spectrometry-based proteomics , 2005, Bioinform..

[22]  Bernhard Kluger,et al.  A novel stable isotope labelling assisted workflow for improved untargeted LC–HRMS based metabolomics research , 2013, Metabolomics.

[23]  Chris F. Taylor,et al.  A common open representation of mass spectrometry data and its application to proteomics research , 2004, Nature Biotechnology.

[24]  Matej Oresic,et al.  MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data , 2006, Bioinform..

[25]  Erik Alm,et al.  The correspondence problem for metabonomics datasets , 2009, Analytical and bioanalytical chemistry.

[26]  Matej Oresic,et al.  MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data , 2010, BMC Bioinformatics.

[27]  Robert Burke,et al.  ProteoWizard: open source software for rapid proteomics tools development , 2008, Bioinform..

[28]  S. Neumann,et al.  CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. , 2012, Analytical chemistry.

[29]  G. Siuzdak,et al.  Innovation: Metabolomics: the apogee of the omics trilogy , 2012, Nature Reviews Molecular Cell Biology.

[30]  Louette R. Johnson Lutjens Research , 2006 .

[31]  Kai Stühler,et al.  Retention time alignment algorithms for LC/MS data must consider non-linear shifts , 2009, Bioinform..

[32]  Frank Suits,et al.  Two-dimensional method for time aligning liquid chromatography-mass spectrometry data. , 2008, Analytical chemistry.

[33]  L. Buydens,et al.  Warping methods for spectroscopic and chromatographic signal alignment: a tutorial. , 2013, Analytica chimica acta.