MetSign: A computational platform for high-resolution mass spectrometry-based metabolomics

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

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)7668-7675
Number of pages8
JournalAnalytical Chemistry
Issue number20
StatePublished - Oct 15 2011

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry


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