Literature DB >> 33269929

SmartPeak Automates Targeted and Quantitative Metabolomics Data Processing.

Svetlana Kutuzova1, Pasquale Colaianni1, Hannes Röst2,3,4, Timo Sachsenberg5,6, Oliver Alka5,6, Oliver Kohlbacher5,6,7,8, Bo Burla9, Federico Torta10, Lars Schrübbers1, Mette Kristensen1, Lars Nielsen1, Markus J Herrgård1,11, Douglas McCloskey1.   

Abstract

Technological advances in high-resolution mass spectrometry (MS) vastly increased the number of samples that can be processed in a life science experiment, as well as volume and complexity of the generated data. To address the bottleneck of high-throughput data processing, we present SmartPeak (https://github.com/AutoFlowResearch/SmartPeak), an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of capillary electrophoresis-, gas chromatography-, and liquid chromatography (LC)-MS(/MS) data and high-pressure LC data for targeted and semitargeted metabolomics, lipidomics, and fluxomics experiments. The application allows for an approximate 100-fold reduction in the data processing time compared to manual processing while enhancing quality and reproducibility of the results.

Year:  2020        PMID: 33269929     DOI: 10.1021/acs.analchem.0c03421

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  3 in total

Review 1.  New software tools, databases, and resources in metabolomics: updates from 2020.

Authors:  Biswapriya B Misra
Journal:  Metabolomics       Date:  2021-05-11       Impact factor: 4.290

Review 2.  A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics.

Authors:  Nils Hoffmann; Gerhard Mayer; Canan Has; Dominik Kopczynski; Fadi Al Machot; Dominik Schwudke; Robert Ahrends; Katrin Marcus; Martin Eisenacher; Michael Turewicz
Journal:  Metabolites       Date:  2022-06-23

3.  Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics.

Authors:  Miao Tian; Zhonglong Lin; Xu Wang; Jing Yang; Wentao Zhao; Hongmei Lu; Zhimin Zhang; Yi Chen
Journal:  Molecules       Date:  2021-05-05       Impact factor: 4.411

  3 in total

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