MetSign: A Computational Platform for High-Resolution Mass Spectrometry-Based Metabolomics
journal contributionposted on 15.10.2011, 00:00 by Xiaoli Wei, Wenlong Sun, Xue Shi, Imhoi Koo, Bing Wang, Jun Zhang, Xinmin Yin, Yunan Tang, Bogdan Bogdanov, Seongho Kim, Zhanxiang Zhou, Craig McClain, Xiang Zhang
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.
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analysis stepinput datadata setsdata mining approachComputational Platformhepatic livermetabolitetime course analysissoftware packagesmouse liversample setspeak list alignmenttime course trajectorybioinformatics toolssignificance testsmzXML formatparameter settingsMetSignanalysis stepsunsupervised pattern recognitionanalysis proceduresdata deconvolution