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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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Quantification of known and unknown terpenoid organosulfates in PM10 using untargeted LC–HRMS/MS: contrasting summertime rural Germany and the North China Plain

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Data Dependent Peak Model Based Spectrum Deconvolution for Analysis of High Resolution LC-MS Data

TL;DR: A data dependent peak model (DDPM) based spectrum deconvolution method was developed for analysis of high resolution LC-MS data and demonstrates that the proposed DDPM spectrum deconVolution method outperforms the traditional method.
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Addressing Main Challenges Regarding Short- and Medium-Chain Chlorinated Paraffin Analysis Using GC/ECNI-MS and LC/ESI-MS Methods.

TL;DR: Investigation of potential issues faced when comparing data from different mass spectrometry platforms and quantification methods suggested that response pattern comparison should only be performed between similar instrumental set-ups, and experimental chlorination degrees should be used when quantifying according to the %Cl.
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Profiling, quantification and classification of cocoa beans based on chemometric analysis of carbohydrates using hydrophilic interaction liquid chromatography coupled to mass spectrometry.

TL;DR: This study provides, for the first time in a large set of samples, a comprehensive absolute quantitative data set for the carbohydrates identified in cocoa beans (fructose, glucose, mannitol, myo-inositol, sucrose, melibiose, raffinose and stachyose).
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SIMA: simultaneous multiple alignment of LC/MS peak lists.

TL;DR: A novel automated procedure for alignment of peak lists from multiple LC/MS runs that combines hierarchical pairwise correspondence estimation with simultaneous alignment and global retention time correction and yields competitive and superior performance on real-world data.
References
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