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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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Identification of the Bacterial Biosynthetic Gene Clusters of the Oral Microbiome Illuminates the Unexplored Social Language of Bacteria during Health and Disease

TL;DR: The number of known biosynthetic pathways present in the oral microbiome is greatly expanded to provide an atlas for experimental characterization of these abundant, yet poorly understood, molecules and socio-chemical relationships, which impact the development of caries and periodontitis, two of the world’s most common chronic diseases.
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Quantitative analysis of glycerophospholipids by LC–MS: Acquisition, data handling, and interpretation

TL;DR: The need for a robust lipidomics workflow including deisotoping, differentiation from background noise, use of multiple internal standards per lipid class, and the use of a scriptable environment is brought into sharp focus in order to create maximum user flexibility and maintain metadata on the parameters of the data analysis as it occurs.
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Biochemometrics to Identify Synergists and Additives from Botanical Medicines: A Case Study with Hydrastis canadensis (Goldenseal)

TL;DR: The current study highlights the importance of fractionation when utilizing metabolomics for identifying bioactive components from botanical extracts and demonstrates the power of SR plots to help merge and interpret complex biological and chemical data sets.
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Deep Neural Networks for Classification of LC-MS Spectral Peaks

TL;DR: Application of machine learning for non-targeted LC-MS based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics datasets.
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.
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