biostudies-literatureWei XNIDDK NIH HHSNIAAA NIH HHSNIGMS NIH HHS7668-75https://www.ebi.ac.uk/biostudies/studies/S-EPMC3196362biostudies-literatureUnknown83(20)Data analysis in metabolomics is currently a major challenge, particularly when large sample sets are analyzed. Herein, we present a novel computational platform entitled MetSign for high-resolution mass spectrometry-based metabolomics. By converting the instrument raw data into mzXML format as its input data, MetSign provides a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment, peak list alignment, normalization, statistical significance tests, unsupervised pattern recognition, and time course analysis. MetSign uses a modular design and an interactive visual data mining approach to enable efficient extraction of useful patterns from data sets. Analysis steps, designed as containers, are presented with a wizard for the user to follow analyses. Each analysis step might contain multiple analysis procedures and/or methods and serves as a pausing point where users can interact with the system to review the results, to shape the next steps, and to return to previous steps to repeat them with different methods or parameter settings. Analysis of metabolite extract of mouse liver with spiked-in acid standards shows that MetSign outperforms the existing publically available software packages. MetSign has also been successfully applied to investigate the regulation and time course trajectory of metabolites in hepatic liver.Analytical chemistryMetSign: a computational platform for high-resolution mass spectrometry-based metabolomics.PMC3196362R01AA018869R01AA018844R01AA015970RC2 AA019385-02P01 AA017103-02R01 GM087735-03R01 AA018844-02R01 DK071765-03R01 DK071765R37 AA010762R01 AA0180161R01GM087735R01 AA015970-03P30 AA019360-02P01AA017103R01AA0180161RC2AA019385P30AA019360RC2 AA019385R01 AA018869R01 AA015970R01 GM087735R01 AA018869-03R37 AA010762-10R01DK7071765R37AA010762P01 AA017103P30 AA019360R01 AA018844R01 AA018016-04Koo IYin XWang BZhang JWei XSun WBogdanov BShi XZhou ZKim SMcClain CZhang XTang YfalseMetSign: a computational platform for high-resolution mass spectrometry-based metabolomics.Data analysis in metabolomics is currently a major challenge, particularly when large sample sets are analyzed. Herein, we present a novel computational platform entitled MetSign for high-resolution mass spectrometry-based metabolomics. By converting the instrument raw data into mzXML format as its input data, MetSign provides a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment, peak list alignment, normalization, statistical significance tests, unsupervised pattern recognition, and time course analysis. MetSign uses a modular design and an interactive visual data mining approach to enable efficient extraction of useful patterns from data sets. Analysis steps, designed as containers, are presented with a wizard for the user to follow analyses. Each analysis step might contain multiple analysis procedures and/or methods and serves as a pausing point where users can interact with the system to review the results, to shape the next steps, and to return to previous steps to repeat them with different methods or parameter settings. Analysis of metabolite extract of mouse liver with spiked-in acid standards shows that MetSign outperforms the existing publically available software packages. MetSign has also been successfully applied to investigate the regulation and time course trajectory of metabolites in hepatic liver.2011-01-01T00:00:00Z2011 Oct2020-11-01T08:40:11Z2019-03-27T00:45:07ZS-EPMC31963622193282810.1021/ac2017025