IPO: a tool for automated optimization of XCMS parameters

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Abstract

Background

Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing.

Results

We implemented the software package IPO (‘Isotopologue Parameter Optimization’) which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments.

IPO optimizes XCMS peak picking parameters by using natural, stable ¹³C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third.

Conclusions

IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data.

The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO. The training sets and test sets can be downloaded from https://health.joanneum.at/IPO.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0562-8) contains supplementary material, which is available to authorized users.

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XCMS Online: a web-based platform to process untargeted metabolomic data.

Ralf Tautenhahn, Gary J Patti, Duane Rinehart … (2012)

Recently, interest in untargeted metabolomics has become prevalent in the general scientific community among an increasing number of investigators. The majority of these investigators, however, do not have the bioinformatic expertise that has been required to process metabolomic data by using command-line driven software programs. Here we introduce a novel platform to process untargeted metabolomic data that uses an intuitive graphical interface and does not require installation or technical expertise. This platform, called XCMS Online, is a web-based version of the widely used XCMS software that allows users to easily upload and process liquid chromatography/mass spectrometry data with only a few mouse clicks. XCMS Online provides a solution for the complete untargeted metabolomic workflow including feature detection, retention time correction, alignment, annotation, statistical analysis, and data visualization. Results can be browsed online in an interactive, customizable table showing statistics, chromatograms, and putative METLIN identities for each metabolite. Additionally, all results and images can be downloaded as zip files for offline analysis and publication. XCMS Online is available at https://xcmsonline.scripps.edu.

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CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.

Carsten Kuhl, Ralf Tautenhahn, Christoph Böttcher … (2012)

Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottleneck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion modes, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis. © 2011 American Chemical Society

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Highly sensitive feature detection for high resolution LC/MS

Ralf Tautenhahn, Christoph Böttcher, Steffen Neumann (2008)

Background Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features – a reliable feature detection is mandatory. Results We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity. Conclusion The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from

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Author and article information

Contributors

Gunnar Libiseller: gunnar.libiseller@joanneum.at

Michaela Dvorzak: michaela.dvorzak@joanneum.at

Ulrike Kleb: ulrike.kleb@joanneum.at

Edgar Gander: edgar.gander@joanneum.at

Tobias Eisenberg: tobias.eisenberg@uni-graz.at

Frank Madeo: frank.madeo@uni-graz.at

Steffen Neumann: sneumann@ipb-halle.de

Gert Trausinger: gert.trausinger@joanneum.at

Frank Sinner: frank.sinner@joanneum.at

Thomas Pieber: thomas.pieber@medunigraz.at

Christoph Magnes: ca.health@joanneum.at

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Journal ID (iso-abbrev): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date (Electronic): 16 April 2015

Publication date PMC-release: 16 April 2015

Publication date Collection: 2015

Volume: 16

Issue: 1

Electronic Location Identifier: 118

Affiliations

[ ]Joanneum Research Forschungsgesellschaft m.b.H., HEALTH, Institute for Biomedicine and Health Sciences, Graz, Austria

[ ]Joanneum Research Forschungsgesellschaft m.b.H., POLICIES, Institute for Economic and Innovation Research, Graz, Austria

[ ]Institute of Molecular Biosciences, NAWI Graz, University of Graz, 8010 Graz, Austria

[ ]BioTechMed Graz, 8010 Graz, Austria

[ ]Department of Stress- and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany

[ ]Department of Internal Medicine, Medical University of Graz, Graz, Austria

Article

Publisher ID: 562

DOI: 10.1186/s12859-015-0562-8

PMC ID: 4404568

PubMed ID: 25888443

SO-VID: e3c64bea-375c-438f-9824-b94423bc9d6d

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 12 November 2014

Date accepted : 30 March 2015

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: metabolomics,xcms,parameter optimization,design of experiments,isotopologue

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: metabolomics, xcms, parameter optimization, design of experiments, isotopologue

IPO: a tool for automated optimization of XCMS parameters

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Most cited references 20

XCMS Online: a web-based platform to process untargeted metabolomic data.

CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.

Highly sensitive feature detection for high resolution LC/MS

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