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Open AccessJournal ArticleDOI

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

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TLDR
A new generation of a popular open-source data processing toolbox, MZmine 2 is introduced, suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
Abstract
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. 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. 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.

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Book ChapterDOI

Metabolomics Data Preprocessing Using ADAP and MZmine 2.

TL;DR: This chapter summarizes the algorithmic principles underlying key steps in the two workflows and illustrates how to apply ADAP to preprocess LC-MS and GC-MS data.
Journal ArticleDOI

Sensitive untargeted identification of short hydrophilic peptides by high performance liquid chromatography on porous graphitic carbon coupled to high resolution mass spectrometry.

TL;DR: An untargeted approach for peptide identification was pursued, based on precursor identification in database with all possible combinations of the 20 natural amino acids and fragmentation spectra matching to in silico generated spectra resulting in the identification of 30 short peptides.
Journal ArticleDOI

Enhanced metabolite annotation via dynamic retention time prediction: Steroidogenesis alterations as a case study

TL;DR: This work proposes an innovative approach based on the chromatographic linear solvent strength (LSS) theory, allowing retention times under any gradient conditions at fixed temperature, stationary phase and mobile phase type to be predicted and facilitating steroid identification.
Journal ArticleDOI

Stable Isotope Labeling-Assisted Metabolite Probing for Emerging Contaminants in Plants.

TL;DR: A novel analytical framework coupling deuterium stable isotope labeling with high-resolution mass spectrometry (SILAMS) in identifying plant biotransformation products is developed and demonstrated to improve the understanding of their food safety and human health risks and potential deleterious effects on other species living on plants.
References
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