Data processing for mass spectrometry-based metabolomics

Mikko Katajamaa (Corresponding Author), Matej Oresic

Research output: Contribution to journalArticleScientificpeer-review

376 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)318-328
JournalJournal of Chromatography A
Volume1158
Issue number1-2
DOIs
Publication statusPublished - 2007
MoE publication typeA1 Journal article-refereed

Fingerprint

Metabolomics
Mass spectrometry
Mass Spectrometry
Metabolites
Data handling
Liquid chromatography
Liquid Chromatography
Software
Experiments
Technology

Keywords

  • Metabolomics
  • Lipidomics
  • Proteomics
  • Normalization
  • Alignment
  • Liquid chromatography
  • Mass spectrometry
  • Feature extraction
  • Peak detection
  • Deconvolution

Cite this

Katajamaa, Mikko ; Oresic, Matej. / Data processing for mass spectrometry-based metabolomics. In: Journal of Chromatography A. 2007 ; Vol. 1158, No. 1-2. pp. 318-328.
@article{2e887eb8e5f843a9a6dc16b7a1e5a45e,
title = "Data processing for mass spectrometry-based metabolomics",
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.",
keywords = "Metabolomics, Lipidomics, Proteomics, Normalization, Alignment, Liquid chromatography, Mass spectrometry, Feature extraction, Peak detection, Deconvolution",
author = "Mikko Katajamaa and Matej Oresic",
year = "2007",
doi = "10.1016/j.chroma.2007.04.021",
language = "English",
volume = "1158",
pages = "318--328",
journal = "Journal of Chromatography A",
issn = "0021-9673",
publisher = "Elsevier",
number = "1-2",

}

Data processing for mass spectrometry-based metabolomics. / Katajamaa, Mikko (Corresponding Author); Oresic, Matej.

In: Journal of Chromatography A, Vol. 1158, No. 1-2, 2007, p. 318-328.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Data processing for mass spectrometry-based metabolomics

AU - Katajamaa, Mikko

AU - Oresic, Matej

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Metabolomics

KW - Lipidomics

KW - Proteomics

KW - Normalization

KW - Alignment

KW - Liquid chromatography

KW - Mass spectrometry

KW - Feature extraction

KW - Peak detection

KW - Deconvolution

U2 - 10.1016/j.chroma.2007.04.021

DO - 10.1016/j.chroma.2007.04.021

M3 - Article

VL - 1158

SP - 318

EP - 328

JO - Journal of Chromatography A

JF - Journal of Chromatography A

SN - 0021-9673

IS - 1-2

ER -