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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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The WEIZMASS spectral library for high-confidence metabolite identification

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Volatiles in Inter-Specific Bacterial Interactions

TL;DR: The identity and antimicrobial activity of volatiles produced by the bacteria were determined in monoculture as well in pairwise combinations and revealed that inter-specific bacterial interactions affectedvolatiles blend composition.
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Metabolomics deciphers the host resistance mechanisms in wheat cultivar Sumai-3, against trichothecene producing and non-producing isolates of Fusarium graminearum.

TL;DR: Non-targeted metabolic profiling was applied to elucidate the host resistance mechanisms to FHB spread through rachis of wheat cultivar Sumai-3 against both trichothecene producing and non-producing isolates of Fusarium graminearum and suggested enhancement of resistance in wheat against FHB can be exploited through stacking of candidate phenylpropanoid pathway genes.
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Frailty markers comprise blood metabolites involved in antioxidation, cognition, and mobility.

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Isotopic labeling-assisted metabolomics using LC-MS.

TL;DR: LC–HRMS enables determination of hundreds to thousands of metabolites over a wide range of concentrations and has developed into one of the most powerful techniques in non-targeted metabolomics.
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