MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data

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, amo...

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Veröffentlicht in:BMC bioinformatics Jg. 11; H. 1; S. 395
Hauptverfasser: Pluskal, Tomáš, Castillo, Sandra, Villar-Briones, Alejandro, Orešič, Matej
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 23.07.2010
BioMed Central Ltd
BMC
Schlagworte:
Algorithms
Applications software
Bioinformatics
Biomedical and Life Sciences
Computational biology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Genetic algorithms
Life Sciences
Mass Spectrometry
Microarrays
Proteomics
Proteomics - methods
Software
Software Design
Japan
RANSAC Algorithm
Master List
Peak List
Mass Spectrometry Data
Peak Picking
ISSN:1471-2105, 1471-2105
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Zusammenfassung: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, MS n 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.
Bibliographie:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-11-395