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.read more
Citations
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Deep learning meets metabolomics: a methodological perspective.
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TL;DR: The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.
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TL;DR: 3,4-dibromopyrrole-2,5-dione is a potent EPI and deserves further attention as an adjuvant to enhance the effectiveness of existing antibiotics.
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MS-CleanR: A Feature-Filtering Workflow for Untargeted LC-MS Based Metabolomics.
Ophélie Fraisier-Vannier,Justine Chervin,Guillaume Cabanac,Virginie Puech,Sylvie Fournier,Virginie Durand,Aurélien Amiel,Olivier André,Omar Abdelaziz Benamar,Bernard Dumas,Hiroshi Tsugawa,Guillaume Marti +11 more
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Untargeted mass spectrometry-based metabolomics approach unveils molecular changes in raw and processed foods and beverages.
Julia M. Gauglitz,Julia M. Gauglitz,Christine M. Aceves,Alexander A. Aksenov,Gajender Aleti,Jehad Almaliti,Amina Bouslimani,Elizabeth Brown,Anaamika Campeau,Andrés Mauricio Caraballo-Rodríguez,Rama Chaar,Ricardo Silva,Alyssa M. Demko,Francesca Di Ottavio,Emmanuel O. Elijah,Madeleine Ernst,L. Paige Ferguson,Xavier Holmes,Alan K. Jarmusch,Lingjing Jiang,Kyo Bin Kang,Irina Koester,Brian Kwan,Jie Li,Yueying Li,Alexey V. Melnik,Carlos Molina-Santiago,Bohan Ni,Aaron L. Oom,Morgan Panitchpakdi,Daniel Petras,Robert A. Quinn,Nicole Sikora,Katharina Spengler,Bahar Teke,Anupriya Tripathi,Sabah Ul-Hasan,Justin J. J. van der Hooft,Fernando Vargas,Alison Vrbanac,Anthony Q. Vu,Steven C Wang,Kelly C. Weldon,Kayla Wilson,Jacob M. Wozniak,Michael Yoon,Nuno Bandeira,Pieter C. Dorrestein,Pieter C. Dorrestein +48 more
TL;DR: The data analysis workflow presented implements an approach to study changes in food chemistry that can reveal global alterations in chemical profiles, identify changes in abundance, as well as identify specific chemicals and their transformation products.
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