Cited by 617Metabolomics and Mass Spectrometry StudiesAdvanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and Applications

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Data processing for mass spectrometry-based metabolomics

Katajamaa, Mikko, Orešič, Matej·2007·Journal of Chromatography A

DOI 10.1016/j.chroma.2007.04.021

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Abstract

Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results. Increasing interest in metabolomics thus led to resurgence of interest in related data processing. A wide variety of methods and software tools have been developed for metabolomics during recent years, and this trend is likely to continue. In this paper we overview the key steps of metabolomic data processing and focus on reviewing recent literature related to this topic, particularly on methods for handling data from liquid chromatography mass spectrometry (LC-MS) experiments.

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Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology
Ballereau, S. J., Glaab, E., Kolodkin, A. N., Chaiboonchoe, A., Biryukov, M., Vlassis, N. A., Ahmed, H., Pellet, J., Baliga, N. S., Hood, L., et al. (2013) · Springer eBooks · doi:10.1007/978-94-007-6803-1_1 · Cited by 10
“These include image processing [190, 191], normalisation [192] and summarisation approaches [193, 194], for microarray gene expression data [195], and raw data filtering [196], peak detection [197], peak alignment [198] and retention time normalisation methods for proteomics and metabolomics mass spectrometry data [19…”
Method
Mass Spectrometry-Driven Proteomics: An Introduction
Helsens, K., Martens, L., Vandekerckhove, J. S., & Gevaert, K. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-148-2_1 · Cited by 11
Metallomics and Metabolomics of Plants Under Environmental Stress Caused by Metals
Ariza, J. L. G., García‐Barrera, T., García-Sevillano, M. Á., González-Fernández, M., & Gómez-Jacinto, V. (2013) · Springer eBooks · doi:10.1007/978-3-642-38469-1_10 · Cited by 11
“are needed for extracting information from data and further analysis (Katajamaa and Oresic 2007).”
Background
Chemometrics, Mass Spectrometry, and Foodomics
Skov, T., & Engelsen, S. B. (2013) · Wiley eBooks · doi:10.1002/9781118537282.ch19 · Cited by 11
“Extract metabolites using dedicated software (commercial or freeware (Castillo et al., 2011; Katajamaa and Orešic, 2007)) and collect these in a data table comprised of “m/z-elution time” pairs.”
Method
Hyphenated techniques for global metabolite profiling
Theodoridis, G. A., & Wilson, I. D. (2008) · Journal of Chromatography B · doi:10.1016/j.jchromb.2008.07.003 · Cited by 11
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. (2024) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-024-05425-3 · Cited by 11
“However, these algorithms, independent from their source, must be optimized for a given analysis to maximize the true positive rate while maintaining low rate of false positives [24, 25].”
EasyLCMS: an asynchronous web application for the automated quantification of LC-MS data
Fructuoso, S., Sevilla, A., Bernal, C., Lozano, A. B., Iborra, J. L., & Cánovas, M. (2012) · BMC Research Notes · doi:10.1186/1756-0500-5-428 · Cited by 10
“In terms of raw data analysis, several platforms have been developed for the quantitative approach, although most of them were designed for GC-MS or focus on proteomics data [3].”
Background
Global Metabolic Profiling Using Ultra-Performance Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry
Qi, Y., Song, Y., Gu, H., Fan, G., & Chai, Y. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1258-2_2 · Cited by 10
Identification of Unknown Substances in Ambient Air (PM10), Profiles and Differences between Rural, Urban and Industrial Areas
López, A., Fuentes-Ferragud, E., Yusà, V., Ibáñez, M., & Coscollà, C. (2022) · Toxics · doi:10.3390/toxics10050220 · Cited by 10
“The acquisition in full-scan mode allowed us to obtain an individual footprint for each evaluated sample [48,49].”
Method
Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics
Traquete, F., Luz, J., CORDEIRO, C. A., Silva, M. S., & Ferreira, A. E. N. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.917911 · Cited by 12
Metabolite profiling to discriminate different species and genus from thistles in Korea using liquid chromatography with quadrupole time‐of‐flight mass spectrometry
Ha, I. J., Lee, M. Y., Kwon, Y., Jung, Y., Kim, H. K., & Hwang, G. (2014) · Journal of Separation Science · doi:10.1002/jssc.201400802 · Cited by 10
Automated And Unbiased Analysis of Lc–Ms Metabolomic Data
Hnatyshyn, S. Y., & Shipkova, P. A. (2012) · Bioanalysis · doi:10.4155/bio.12.9 · Cited by 10
A hybrid retention time alignment algorithm for SWATH‐MS data
Wu, L., Amon, S., & Lam, H. (2016) · PROTEOMICS · doi:10.1002/pmic.201500511 · Cited by 10
“For a more comprehensive survey of LC-MS alignment methods, readers are referred to reviews [8,9,37] and benchmarking studies [38–40].”
Method
Integrating Molecular Network and Culture Media Variation to Explore the Production of Bioactive Metabolites by Vibrio diabolicus A1SM3
Conde-Martínez, N., Bauermeister, A., Pilon, A. C., Lopes, N. P., & Tello, E. (2019) · Marine Drugs · doi:10.3390/md17040196 · Cited by 10
A Metabolomics-Inspired Strategy for the Identification of Protein Covalent Modifications
Nunes, J., Charneira, C., Nunes, C., Gouveia-Fernandes, S., Serpa, J., Morelló, J., & Antunes, A. M. M. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00532 · Cited by 10
Metabolic Impairments Caused by a “Chemical Cocktail” of DDE and Selenium in Mice Using Direct Infusion Triple Quadrupole Time-of-Flight and Gas Chromatography–Mass Spectrometry
Rodríguez‐Moro, G., Abril, N., Jara-Biedma, R., Ramírez‐Acosta, S., Gómez‐Ariza, J. L., & García‐Barrera, T. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00102 · Cited by 10
Metabolomics Analysis in Food Authentication
Zhang, J., Hu, Q., Yu, Q., Chen, Y., Zhao, Y., & Qie, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-100596-5.22793-5 · Cited by 10
High‐resolution mass spectrometry (HRMS): Focus on the m/z values estimated by the Savitzky–Golay first derivative
Boulet, J., Meudec, E., Vallverdú‐Queralt, A., & Cheynier, V. (2021) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.9036 · Cited by 10
Metabolomics workflow as a driven tool for rapid detection of metabolites in doping analysis. Development and validation
Leogrande, P., Jardines, D., Brito, D. M., Domenici, E., de la Torre, X., Parr, M. K., & Botrè, F. (2021) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.9217 · Cited by 10
Metabolomics in Fruit Development
Hanhineva, K., & Aharoni, A. (2009) · Springer eBooks · doi:10.1007/978-90-481-2967-6_29 · Cited by 10
“The data is subsequently processed using diverse mining tools including multivariate analyses and clustering for discovering differences between groups of samples (Katajamaa and Orešič 2007).”
Method
Single-Embryo Metabolomics and Systematic Prediction of Developmental Stage in Zebrafish
Hayashi, S., Yoshida, M., Fujiwara, T., Maegawa, S., & Fukusaki, E. (2011) · Zeitschrift für Naturforschung C · doi:10.1515/znc-2011-3-414 · Cited by 11
“…processing and multivariate analysis Raw chromatographic data were converted into ANDI fi les (analytical data interchange protocol, *.cdf), and these data were subject to peak detection and alignment, and fi ltered of gap using MZmine software (Katajamaa et al., 2006; Katajamaa and Oresic, 2007).”
Method
Application of Fragment Ion Information as Further Evidence in Probabilistic Compound Screening Using Bayesian Statistics and Machine Learning: A Leap Toward Automation
Woldegebriel, M., Zomer, P., Mol, H. G. J., & Vivó‐Truyols, G. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b01630 · Cited by 9
Role of metabolomics in identifying cardiac hypertrophy: an overview of the past 20 years of development and future perspective
Singh, S. P., Sethi, R., Saxena, S. K., & Gupta, A. K. (2021) · Expert Reviews in Molecular Medicine · doi:10.1017/erm.2021.12 · Cited by 11
Metabolomics Data Treatment: Basic Directions of the Full Process
Zamora-Obando, H. R., Duarte, G. H. B., & Simionato, A. V. C. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-77252-9_12 · Cited by 9
Pure ion chromatogram extraction via optimal k-means clustering
Ji, H., Lü, H., & Zhang, Z. (2016) · RSC Advances · doi:10.1039/c6ra08409e · Cited by 9
A Web Service Framework for Interactive Analysis of Metabolomics Data
Lyutvinskiy, Y., Watrous, J. D., Jain, M., & Nilsson, R. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b00890 · Cited by 9
2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes
Luzarowski, M., Wojciechowska, I., & Skirycz, A. (2018) · Journal of Visualized Experiments · doi:10.3791/57720 · Cited by 9
Metabolomic study of bioactive compounds in strawberries preserved under controlled atmosphere based on GC‐MS and DI‐ESI‐QqQ‐TOF‐MS
Ramírez‐Acosta, S., Arias‐Borrego, A., Gómez‐Ariza, J. L., & García‐Barrera, T. (2018) · Phytochemical Analysis · doi:10.1002/pca.2805 · Cited by 9
A volatolomic approach using cerumen as biofluid to diagnose bovine intoxication by Stryphnodendron rotundifolium
Barbosa, J. M. G., Rodrigues, M. K. F., David, L. C., Silva, T. C. E., Lima, D. A. F., Pereira, N. Z., D′Alessandro, E. B., de Oliveira, A. E., da Cunha, P. H. J., Fíoravantí, M. C. S., et al. (2020) · Biomedical Chromatography · doi:10.1002/bmc.4935 · Cited by 9
Post-optimization of Py-GC/MS data: A case study using a new digital chemical noise reduction filter (NOISERA) to enhance the data quality utilizing OpenChrom mass spectrometric software
Wenig, P. (2011) · Journal of Analytical and Applied Pyrolysis · doi:10.1016/j.jaap.2011.05.013 · Cited by 9
A review of study designs and data analyses in metabolomics studies in myopia
Hou, X.-W., Wang, Y., Wu, Q., Ke, C., & Pan, C. (2022) · Analytical Biochemistry · doi:10.1016/j.ab.2022.114850 · Cited by 8
qPeaks: A Linear Regression-Based Asymmetric Peak Model for Parameter-Free Automatized Detection and Characterization of Chromatographic Peaks in Non-Target Screening Data
Reuschenbach, M., Drees, F., Leupold, M. S., Tintrop, L. K., Schmidt, T. C., & Renner, G. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c00494 · Cited by 8

Authors: Katajamaa, M., & Orešič, M.
Affiliations: Turku Centre for Biotechnology (FI)
VTT Technical Research Centre of Finland (FI)
Corresponding author: Katajamaa, Mikko
Orešič, Matej
ORCID: Orešič, Matej
Venue: Journal of Chromatography A
Publisher: Elsevier BV
Published: 2007-07
Volume: 1158
Issue: 1-2
Pages: 318-328
ISSN: 0021-9673 (ISSN-L)
DOI: 10.1016/j.chroma.2007.04.021
OpenAlex: W2075109094
Cited by: 616
References: 82
Citations by year: 2026: 13 · 2025: 10 · 2024: 21 · 2023: 10 · 2022: 19 · 2021: 32 · 2020: 27 · 2019: 37 · 2018: 38 · 2017: 33 · 2016: 47 · 2015: 38 · 2014: 50 · 2013: 54 · 2012: 44
FWCI: 2.56
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Chromatography A (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
Keywords: metabolomics data processing, mass spectrometry data, liquid chromatography-mass spectrometry, metabolite identification, metabolite quantification, data processing software
MeSH terms: Chromatography, Liquid, Mass Spectrometry

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