We are analyzing https://link.springer.com/article/10.1186/1471-2105-11-395.

Title:
MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data | BMC Bioinformatics
Description:
Background 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. Results 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. Conclusions 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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Keywords {🔍}

data, peak, mzmine, article, pubmed, ransac, alignment, peaks, processing, mass, algorithm, figure, google, scholar, model, software, results, aligner, modules, cas, list, retention, time, samples, table, development, values, methods, support, visualization, identification, file, bioinformatics, full, spectrometry, authors, central, method, original, intensity, join, search, online, metabolomic, introduced, obtained, project, provided, detection, noise,

Topics {✒️}

mass spectrometry-based metabolomics lc/ms profile data lc-ms/ms proteomics data flight-mass spectrometry method ms-based data processing open-source software toolbox high resolution lc/ms mass spectrometry data open-source java library tomáš pluskal lc-ms analytical platform high-resolution ms data high-resolution data suggests potentially toxic statin-induced specialized data-processing methods data analysis workflow high-resolution spectra processing locally-weighted scatterplot smoothing author information authors article pluskal probability-based protein identification 3d view lc-ms proteomics full size image eu-funded project etherpaths open source software lc-ms data peak list showing article download pdf supported file formats raw data import peak list data metabolomics utilizing high mass resolution ms data processing user-friendly graphic interface ms/ms spectra obtained peak picking unit mass resolution open-source libraries local minimum search peak list deisotoping authors’ original file validated capillary electrophoresis-time villar-briones neutral molecular mass master list ms data exchange privacy choices/manage cookies full access

Schema {🗺️}

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         description: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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      description: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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         Master List
         RANSAC Algorithm
         Bioinformatics
         Microarrays
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
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               type:PostalAddress
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      affiliation:
            name:Okinawa Institute of Science and Technology (OIST)
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