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Open AccessJournal ArticleDOI

MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data

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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Citations
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Exploring natural variation of Pinus pinaster Aiton using metabolomics: Is it possible to identify the region of origin of a pine from its metabolites?

TL;DR: Needles were the most sensitive organ, where strong correlations were found between flavonoids and the water regime of the geographic origin of the provenance, and the data obtained point to genome specialization aimed at maximizing the drought stress resistance of trees depending on their origin.
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Non-targeted analysis of unexpected food contaminants using LC-HRMS

TL;DR: The method, study design, and settings in the software were optimized to minimize manual evaluation and faulty or irrelevant hits and to maximize hit rate of the spiked compounds, to improve detection capability at low concentrations.
Journal ArticleDOI

Environmental metabolomics: Databases and tools for data analysis

TL;DR: A boutique database developed in the laboratory for efficient data analysis and selection of mass spectral targets for metabolite identification is described, and a summary of the current state of metabolomics databases is summarized.
Journal ArticleDOI

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

TL;DR: In this article, the authors highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020, and discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data.
References
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Journal ArticleDOI

KEGG: Kyoto Encyclopedia of Genes and Genomes

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

TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Journal ArticleDOI

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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