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

• Published in: BMC bioinformatics Vol. 11; no. 1; p. 395
• Main Authors: Pluskal, Tomáš, Castillo, Sandra, Villar-Briones, Alejandro, Orešič, Matej
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.07.2010; BioMed Central Ltd; BMC
• Subjects: 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
• DOI: 10.1186/1471-2105-11-395

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.