title: MathDAMP: a package for differential analysis of metabolite profiles.
abstract: BACKGROUND
  With the advent of metabolomics as a powerful tool for both functional and biomarker discovery, the identification of specific differences between complex metabolite profiles is becoming a major challenge in the data analysis pipeline. The task remains difficult, given the datasets' size, complexity, and common shifts in migration (elution/retention) times between samples analyzed by hyphenated mass spectrometry methods.
  
  
  RESULTS
  We present a Mathematica (Wolfram Research, Inc.) package MathDAMP (Mathematica package for Differential Analysis of Metabolite Profiles), which highlights differences between raw datasets acquired by hyphenated mass spectrometry methods by applying arithmetic operations to all corresponding signal intensities on a datapoint-by-datapoint basis. Peak identification and integration is thus bypassed and the results are displayed graphically. To facilitate direct comparisons, the raw datasets are automatically preprocessed and normalized in terms of both migration times and signal intensities. A combination of dynamic programming and global optimization is used for the alignment of the datasets along the migration time dimension. The processed datasets and the results of direct comparisons between them are visualized using density plots (axes represent migration time and m/z values while peaks appear as color-coded spots) providing an intuitive overall view. Various forms of comparisons and statistical tests can be applied to highlight subtle differences. Overlaid electropherograms (chromatograms) corresponding to the vicinities of the candidate differences from any result may be generated in a descending order of significance for visual confirmation. Additionally, a standard library table (a list of m/z values and migration times for known compounds) may be aligned and overlaid on the plots to allow easier identification of metabolites.
  
  
  CONCLUSIONS
  Our tool facilitates the visualization and identification of differences between complex metabolite profiles according to various criteria in an automated fashion and is useful for data-driven discovery of biomarkers and functional genomics.
authors.affiliation: Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan. richard.baran@gmail.com <richard.baran@gmail.com>
authors.forename: Richard
authors.initials: R
authors.lastname: Baran
authors.forename: Hayataro
authors.initials: H
authors.lastname: Kochi
authors.forename: Natsumi
authors.initials: N
authors.lastname: Saito
authors.forename: Makoto
authors.initials: M
authors.lastname: Suematsu
authors.forename: Tomoyoshi
authors.initials: T
authors.lastname: Soga
authors.forename: Takaaki
authors.initials: T
authors.lastname: Nishioka
authors.forename: Martin
authors.initials: M
authors.lastname: Robert
authors.forename: Masaru
authors.initials: M
authors.lastname: Tomita
mesh_terms: D015415:Biomarkers / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D004734:Energy Metabolism / Q000502:physiology; D008432:Mathematical Computing
chemical_list: D015415:Biomarkers