Our collaborative shared resource center offers highly flexible data acquisition and analysis approaches for global untargeted (hypothesis-generating) and targeted (hypothesis-driven) metabolomics analyses workflows. CIT team members are glad to guide you in finding the best fit for your project’s purpose.

Global Untargeted Metabolomics Data Analysis Strategies and Metabolite Identifications

In-depth analysis of complex, measurable sample analytes (incl. chemical unknowns) for hypothesis generation

LC-IM-MS/MS and LC-MS/MS for Non-Targeted Global Metabolomics

During acquisition four types of information are obtained for every metabolite observed and specific molecular information can be gleamed from each separation dimension.
(
Adapted from: Patti, G.J; Yanes, O; Siuzdak, G. Nature Reviews, 2012, 13, 263-269.)

  • Data Alignment & Feature Detection

    Application of linear and nonlinear retention time corrections, peak deconvolution and isotope ratio grouping.

  • Feature Prioritization

    Analysis Strategies:

    • Principal Component Analysis
    • Multivariate Statistical Analysis
    • Heat Maps
    • ANOVA/t-test
    • Self Organizing Map Analysis
    • Volcano Plots
    • Network Activity Prediction
  • Metabolite Identification

    Unique feature to validated metabolite identification assignments.

Mass Spec Statistical Analysis Approaches Offered at Vanderbilt

The CIT utilizes various open source and commercially acquired data analysis software packages and libraries for untargeted and targeted analysis including:

  • Mummichog (Emory University School of Medicine, open source)
  • GEDI (Harvard Medical School, open source)
  • Prism (Graph Pad)
  • METLIN (Scripps Center for Metabolomics)
  • HMDB (The Metabolomics Innovation Centre)
  • NIST Standard Reference Materials & Data

Targeted Metabolomics Analysis

Hypothesis-driven, quantitative measurement of known metabolites

Targeted Quantitative LC-MS/MS Analysis of Biospecimen Samples