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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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IPO: a tool for automated optimization of XCMS parameters

TL;DR: The software package IPO (‘Isotopologue Parameter Optimization’) was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices and the potential of IPO to increase the reliability of metabolomics data was shown.
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MetaboAnalystR 2.0: From Raw Spectra to Biological Insights.

TL;DR: This work introduces MetaboAnalystR 2.0, a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment and integrates XCMS and CAMERA to support raw spectral processing and peak annotation.
Journal ArticleDOI

Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra

TL;DR: The broad utility of CANOPUS is demonstrated by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.
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Global urinary metabolic profiling procedures using gas chromatography–mass spectrometry

TL;DR: This protocol facilitates the metabolic profiling of ∼400–600 metabolites in 120 urine samples per week and results obtained using GC/MS are complementary to NMR and LC/MS.
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.
Journal ArticleDOI

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

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Probability-based protein identification by searching sequence databases using mass spectrometry data.

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.
Journal ArticleDOI

Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting

TL;DR: Locally weighted regression as discussed by the authors is a way of estimating a regression surface through a multivariate smoothing procedure, fitting a function of the independent variables locally and in a moving fashion analogous to how a moving average is computed for a time series.
Journal ArticleDOI

METLIN: a metabolite mass spectral database.

TL;DR: METLIN includes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrumetry (MS/MS) Spectra, and LC/MS data.
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