MarVis-Filter: Ranking, Filtering, Adduct and Isotope Correction of Mass Spectrometry Data

Statistical ranking, filtering, adduct detection, isotope correction, and molecular formula calculation are essential tasks in processing mass spectrometry data in metabolomics studies. In order to obtain high-quality data sets, a framework which incorporates all these methods is required. We present the MarVis-Filter software, which provides well-established and specialized methods for processing mass spectrometry data. For the task of ranking and filtering multivariate intensity profiles, MarVis-Filter provides the ANOVA and Kruskal-Wallis tests with adjustment for multiple hypothesis testing. Adduct and isotope correction are based on a novel algorithm which takes the similarity of intensity profiles into account and allows user-defined ionization rules. The molecular formula calculation utilizes the results of the adduct and isotope correction. For a comprehensive analysis, MarVis-Filter provides an interactive interface to combine data sets deriving from positive and negative ionization mode. The software is exemplarily applied in a metabolic case study, where octadecanoids could be identified as markers for wounding in plants.

[1]  Oliver Fiehn,et al.  Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry , 2007, BMC Bioinformatics.

[2]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[3]  Subhabrata Chakraborti,et al.  Nonparametric Statistical Inference , 2011, International Encyclopedia of Statistical Science.

[4]  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.

[5]  V. Shulaev,et al.  Metabolomics for plant stress response. , 2008, Physiologia plantarum.

[6]  I. Feussner,et al.  Biosynthesis of oxylipins in non-mammals. , 2009, Progress in lipid research.

[7]  Lennart Opitz,et al.  The COP9 signalosome mediates transcriptional and metabolic response to hormones, oxidative stress protection and cell wall rearrangement during fungal development , 2010, Molecular microbiology.

[8]  R. Abagyan,et al.  XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. , 2006, Analytical chemistry.

[9]  Burkhard Morgenstern,et al.  Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps , 2008, Algorithms for Molecular Biology.

[10]  Steffen Neumann,et al.  Annotation of LC/ESI-MS Mass Signals , 2007, BIRD.

[11]  David W. Russell,et al.  LMSD: LIPID MAPS structure database , 2006, Nucleic Acids Res..

[12]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[13]  Arjen Lommen,et al.  MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. , 2009, Analytical chemistry.

[14]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[15]  G. Howe,et al.  Plant immunity to insect herbivores. , 2008, Annual review of plant biology.

[16]  M. Orešič,et al.  Data processing for mass spectrometry-based metabolomics. , 2007, Journal of chromatography. A.

[17]  Lloyd W Sumner,et al.  Biomarker metabolites capturing the metabolite variance present in a rice plant developmental period , 2005, BMC Plant Biology.

[18]  Peter Meinicke,et al.  MarVis: a tool for clustering and visualization of metabolic biomarkers , 2009, BMC Bioinformatics.

[19]  David S. Wishart,et al.  HMDB: a knowledgebase for the human metabolome , 2008, Nucleic Acids Res..

[20]  Andreas Günther,et al.  Relatedness of medically important strains of Saccharomyces cerevisiae as revealed by phylogenetics and metabolomics , 2008, Yeast.

[21]  O. Fiehn,et al.  Metabolite profiling for plant functional genomics , 2000, Nature Biotechnology.

[22]  Beat Keller,et al.  The Arabidopsis male-sterile mutant dde2-2 is defective in the ALLENE OXIDE SYNTHASE gene encoding one of the key enzymes of the jasmonic acid biosynthesis pathway , 2002, Planta.

[23]  J. Lindon,et al.  'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. , 1999, Xenobiotica; the fate of foreign compounds in biological systems.

[24]  K. Fraser,et al.  High-throughput direct-infusion ion trap mass spectrometry: a new method for metabolomics. , 2007, Rapid communications in mass spectrometry : RCM.

[25]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[26]  J. Keurentjes,et al.  Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry , 2007, Nature Protocols.

[27]  I. Feussner,et al.  The Alphabet of Galactolipids in Arabidopsis thaliana , 2011, Front. Plant Sci..

[28]  A. Fernie,et al.  Gas chromatography mass spectrometry–based metabolite profiling in plants , 2006, Nature Protocols.

[29]  B. Hammock,et al.  Mass spectrometry-based metabolomics. , 2007, Mass spectrometry reviews.

[30]  I. Feussner,et al.  Methods for the analysis of oxylipins in plants. , 2009, Phytochemistry.

[31]  Angel R. Martinez,et al.  MATLAB Statistics Toolbox , 2001 .

[32]  Knut Reinert,et al.  OpenMS – An open-source software framework for mass spectrometry , 2008, BMC Bioinformatics.

[33]  Peter D. Karp,et al.  MetaCyc and AraCyc. Metabolic Pathway Databases for Plant Research1[w] , 2005, Plant Physiology.

[34]  Yanli Wang,et al.  PubChem: a public information system for analyzing bioactivities of small molecules , 2009, Nucleic Acids Res..

[35]  John Draper,et al.  Representation, comparison, and interpretation of metabolome fingerprint data for total composition analysis and quality trait investigation in potato cultivars. , 2007, Journal of agricultural and food chemistry.