MZmine 2

Modular framework for processing, visualizing, and analyzing mass spectrometrybased molecular profile data

T. Pluskal (Corresponding Author), Sandra Castillo, A. Villar-Briones, Matej Oresic

Research output: Contribution to journalArticleScientificpeer-review

1002 Citations (Scopus)

Abstract

Background
Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.

Results
A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms.

Conclusions
MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
Original languageEnglish
Number of pages11
JournalBMC Bioinformatics
Volume11
Issue number395
DOIs
Publication statusPublished - 2010
MoE publication typeA1 Journal article-refereed

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Molecular mass
Metabolomics
Alignment
Profiling
Visualization
Processing
Data Processing Methods
Scatter diagram
Software Design
Functional Genomics
Module
Systems Biology
Information Storage and Retrieval
Proteomics
Biomarkers
Licensure
Genomics
Isotopes
Open Source
Usability

Cite this

@article{835dfb445648423c9f49a67509268a84,
title = "MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometrybased molecular profile data",
abstract = "BackgroundMass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.ResultsA key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms.ConclusionsMZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.",
author = "T. Pluskal and Sandra Castillo and A. Villar-Briones and Matej Oresic",
year = "2010",
doi = "10.1186/1471-2105-11-395",
language = "English",
volume = "11",
journal = "BMC Bioinformatics",
issn = "1471-2105",
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MZmine 2 : Modular framework for processing, visualizing, and analyzing mass spectrometrybased molecular profile data. / Pluskal, T. (Corresponding Author); Castillo, Sandra; Villar-Briones, A.; Oresic, Matej.

In: BMC Bioinformatics, Vol. 11, No. 395, 2010.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - MZmine 2

T2 - Modular framework for processing, visualizing, and analyzing mass spectrometrybased molecular profile data

AU - Pluskal, T.

AU - Castillo, Sandra

AU - Villar-Briones, A.

AU - Oresic, Matej

PY - 2010

Y1 - 2010

N2 - BackgroundMass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.ResultsA key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms.ConclusionsMZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

AB - BackgroundMass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.ResultsA key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms.ConclusionsMZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

U2 - 10.1186/1471-2105-11-395

DO - 10.1186/1471-2105-11-395

M3 - Article

VL - 11

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - 395

ER -