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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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MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC–MS metabolomics data

TL;DR: This work represents an important step forward in developing an automated tool for filtering out unreliable peak integrations in untargeted LC-MS metabolomics data.
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Multi-tissue integration of transcriptomic and specialized metabolite profiling provides tools for assessing the common bean (Phaseolus vulgaris) metabolome.

TL;DR: A metabolomics approach based on liquid chromatography‐mass spectrometry was used to dissect the chemical composition at a tissue‐specific level in several accessions of common bean belonging to different gene pools, and a particular focus was given to flavonoids, triterpenoid saponins and hydroxycinnamates.
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Application of fast Fourier transform cross-correlation and mass spectrometry data for accurate alignment of chromatograms.

TL;DR: A new alignment algorithm called CAMS (Chromatogram Alignment via Mass Spectra) is present here to correct the retention time shifts among chromatograms accurately and rapidly and demonstrates that CAMS can preserve the shape of peaks and achieve a high quality alignment result.
Journal ArticleDOI

Enhanced phase II detoxification contributes to beneficial effects of dietary restriction as revealed by multi-platform metabolomics studies.

TL;DR: The Nuclear factor (erythroid-derived 2)-like 2 signaling pathway was shown to be up-regulated, providing a mechanistic clue regarding the enhanced phase II detoxification in liver tissue and metabolomic and biochemical studies provide a possible metabolic perspective for understanding the complex mechanism underlying the beneficial effects of DR.
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The Unique Lipidomic Signatures of Saccharina latissima Can Be Used to Pinpoint Their Geographic Origin

TL;DR: Differences in the elemental compositions, fatty acid profiles, and lipidomes of S. latissima originate from three distinct locations make it possible to pinpoint specific lipid species that are likely to represent origin biomarkers, relevant for future applications in the field of geographic origin traceability and food control.
References
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TL;DR: The Kyoto Encyclopedia of Genes and Genomes (KEGG) as discussed by the authors is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules.
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TL;DR: A new computer program, Mascot, is presented, which integrates all three types of search for protein identification by searching a sequence database using mass spectrometry data, and the scoring algorithm is probability based.
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Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting

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