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Processing methods for differential analysis of LC/MS profile data

BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order...

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Detalles Bibliográficos
Autores principales: Katajamaa, Mikko, Orešič, Matej
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1187873/
https://www.ncbi.nlm.nih.gov/pubmed/16026613
http://dx.doi.org/10.1186/1471-2105-6-179
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author Katajamaa, Mikko
Orešič, Matej
author_facet Katajamaa, Mikko
Orešič, Matej
author_sort Katajamaa, Mikko
collection PubMed
description BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data. RESULTS: We present a software package MZmine that enables differential LC/MS analysis of metabolomics data. This software is a toolbox containing methods for all data processing stages preceding differential analysis: spectral filtering, peak detection, alignment and normalization. Specifically, we developed and implemented a new recursive peak search algorithm and a secondary peak picking method for improving already aligned results, as well as a normalization tool that uses multiple internal standards. Visualization tools enable comparative viewing of data across multiple samples. Peak lists can be exported into other data analysis programs. The toolbox has already been utilized in a wide range of applications. We demonstrate its utility on an example of metabolic profiling of Catharanthus roseus cell cultures. CONCLUSION: The software is freely available under the GNU General Public License and it can be obtained from the project web page at: .
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spelling pubmed-11878732005-08-18 Processing methods for differential analysis of LC/MS profile data Katajamaa, Mikko Orešič, Matej BMC Bioinformatics Software BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data. RESULTS: We present a software package MZmine that enables differential LC/MS analysis of metabolomics data. This software is a toolbox containing methods for all data processing stages preceding differential analysis: spectral filtering, peak detection, alignment and normalization. Specifically, we developed and implemented a new recursive peak search algorithm and a secondary peak picking method for improving already aligned results, as well as a normalization tool that uses multiple internal standards. Visualization tools enable comparative viewing of data across multiple samples. Peak lists can be exported into other data analysis programs. The toolbox has already been utilized in a wide range of applications. We demonstrate its utility on an example of metabolic profiling of Catharanthus roseus cell cultures. CONCLUSION: The software is freely available under the GNU General Public License and it can be obtained from the project web page at: . BioMed Central 2005-07-18 /pmc/articles/PMC1187873/ /pubmed/16026613 http://dx.doi.org/10.1186/1471-2105-6-179 Text en Copyright © 2005 Katajamaa and Orešič; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Katajamaa, Mikko
Orešič, Matej
Processing methods for differential analysis of LC/MS profile data
title Processing methods for differential analysis of LC/MS profile data
title_full Processing methods for differential analysis of LC/MS profile data
title_fullStr Processing methods for differential analysis of LC/MS profile data
title_full_unstemmed Processing methods for differential analysis of LC/MS profile data
title_short Processing methods for differential analysis of LC/MS profile data
title_sort processing methods for differential analysis of lc/ms profile data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1187873/
https://www.ncbi.nlm.nih.gov/pubmed/16026613
http://dx.doi.org/10.1186/1471-2105-6-179
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