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.read more
Citations
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Journal ArticleDOI
Multivariate Analysis in Metabolomics.
Bradley Worley,Robert Powers +1 more
TL;DR: The use of multivariate analysis for metabolomics is discussed, as well as common pitfalls and misconceptions, and spectral features contributing most to variation or separation are identified for further analysis.
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Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst
TL;DR: This protocol provides a step-wise description on how to format and upload data to MetaboAnalyst, how to process and normalize data,How to identify significant features and patterns through univariate and multivariate statistical methods and how to use metabolite set enrichment analysis and metabolic pathway analysis to help elucidate possible biological mechanisms.
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CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.
TL;DR: CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data is presented, and a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate is presented.
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The Dynamics of the Human Infant Gut Microbiome in Development and in Progression toward Type 1 Diabetes
Aleksandar Kostic,Aleksandar Kostic,Dirk Gevers,Heli Siljander,Tommi Vatanen,Tommi Vatanen,Tuulia Hyötyläinen,Tuulia Hyötyläinen,Anu-Maaria Hämäläinen,Aleksandr Peet,Vallo Tillmann,Päivi Pöhö,Päivi Pöhö,Ismo Mattila,Ismo Mattila,Harri Lähdesmäki,Eric A. Franzosa,Outi Vaarala,Marcus C. de Goffau,Hermie J. M. Harmsen,Jorma Ilonen,Jorma Ilonen,Suvi M. Virtanen,Suvi M. Virtanen,Clary B. Clish,Matej Orešič,Matej Orešič,Curtis Huttenhower,Curtis Huttenhower,Mikael Knip,Ramnik J. Xavier +30 more
TL;DR: Trends in the development of the human infant gut microbiome along with specific alterations that precede T1D onset and distinguish T2D progressors from nonprogressors are identified.
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Predicting Network Activity from High Throughput Metabolomics
Shuzhao Li,Youngja Park,Youngja Park,Sai Duraisingham,Sai Duraisingham,Fred Strobel,Nooruddin Khan,Nooruddin Khan,Quinlyn A. Soltow,Dean P. Jones,Bali Pulendran,Bali Pulendran +11 more
TL;DR: A set of computational algorithms are presented which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites.
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