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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Method
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From lipids analysis towards lipidomics, a new challenge for the analytical chemistry of the 21st century. Part II: Analytical lipidomics
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Background
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Radiation metabolomics and its potential in biodosimetry
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“Processing raw instrumental files usually includes feature detection, chromatogram building, deisotoping, peak alignment, and gap-filling [86,87].”
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“The data analysis workflow starts with the data processing, which entails noise reduction, peak detection, chromatogram alignment, normalization, and conversion of the processed data to suitable format for further analysis (Brown et al. 2005; Katajamaa and Oresic 2007).”
Method
Region-specific metabolic alterations in the brain of the APP/PS1 transgenic mice of Alzheimer's disease
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Method
Direct Infusion Mass Spectrometry for Metabolomic Phenotyping of Diseases
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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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Data processing for mass spectrometry-based metabolomics

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