MetSign: 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.

[1]  Xiang Zhang,et al.  Data pre-processing in liquid chromatography-mass spectrometry-based proteomics , 2005, Bioinform..

[2]  Deepayan Sarkar,et al.  Detecting differential gene expression with a semiparametric hierarchical mixture method. , 2004, Biostatistics.

[3]  Richard E. Higgs,et al.  Rapid Method To Estimate the Presence of Secondary Metabolites in Microbial Extracts , 2001, Applied and Environmental Microbiology.

[4]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[5]  Kathleen Marchal,et al.  Evaluation of time profile reconstruction from complex two-color microarray designs , 2008, BMC Bioinformatics.

[6]  Gordana Ivosev,et al.  Comprehensive analytical strategy for biomarker identification based on liquid chromatography coupled to mass spectrometry and new candidate confirmation tools. , 2009, Analytical chemistry.

[7]  Xiaomei Yan,et al.  Direct infusion mass spectrometry or liquid chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer. , 2010, The Analyst.

[8]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Coral Barbas,et al.  Gas chromatography-mass spectrometry (GC-MS)-based metabolomics. , 2011, Methods in molecular biology.

[10]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[11]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[12]  J. Antignac,et al.  Development of a metabonomic approach based on LC-ESI-HRMS measurements for profiling of metabolic changes induced by recombinant equine growth hormone in horse urine , 2009, Analytical and bioanalytical chemistry.

[13]  John M Baker,et al.  Recent applications of NMR spectroscopy in plant metabolomics , 2007, The FEBS journal.

[14]  T. Bathen,et al.  Magnetic resonance metabolomics of intact tissue: a biotechnological tool in cancer diagnostics and treatment evaluation. , 2010, Cancer research.

[15]  Xin Lu,et al.  Integrated GC-MS and LC-MS plasma metabonomics analysis of ankylosing spondylitis. , 2008, The Analyst.

[16]  Wanchang Lin,et al.  Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules' , 2009, BMC Bioinformatics.

[17]  G. Siuzdak,et al.  XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. , 2008, Analytical chemistry.

[18]  Steffen Neumann,et al.  Highly sensitive feature detection for high resolution LC/MS , 2008, BMC Bioinformatics.

[19]  F. E. Grubbs.  Procedures for Detecting Outlying Observations in Samples , 1969 .

[20]  Matej Oresic,et al.  MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data , 2010, BMC Bioinformatics.

[21]  Marko Grobelnik,et al.  Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers , 2006, SLSFS.