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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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Citations
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DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data.

TL;DR: In this article, the authors proposed a data-dependent-assisted data independent acquisition (DaDIA) data acquisition workflow that combines DDA and DIA analyses to achieve better metabolomic data quality, including enhanced metabolome coverage, tandem mass spectrometry (MS2) coverage and MS2 quality.
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Bioprospecting Chemical Diversity and Bioactivity in a Marine Derived Aspergillus terreus

TL;DR: This study demonstrates that LC/MS based multivariate analysis provides a simple yet powerful tool to analyze the metabolome of a single fungal strain grown under various conditions and uses these differences to guide the discovery of new bioactive molecules.
Journal ArticleDOI

Identification of bioactive short peptides in cow milk by high-performance liquid chromatography on C18 and porous graphitic carbon coupled to high-resolution mass spectrometry.

TL;DR: An untargeted peptidomic approach based on ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry was developed for the identification of short peptides in cow milk samples, and indicated that an integrated approach may be appropriate to improve the separation of different peptides and increase the number of identifications because of the wide range of polarity of long peptides.
Journal ArticleDOI

Introduction to Cheminformatics

TL;DR: The intent of this unit is to show how cheminformatics not only shares many similarities with the field of bioinformatics, but also enhances much of what is currently done in bioinformics, molecular biology, and biochemistry.
Book ChapterDOI

Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration.

TL;DR: The whole pipeline of metabolomics study is discussed, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.
References
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