This course will feature hands-on training with real-world metabolomics data covering LC/MS compound identification, data processing, statistical analysis, network mapping and data interpretation. The WCMC will provide laptops for all participants and utilize open source software where possible
The LC-MS/MS compound identification part will cover the annotation and identification of unknown compounds using MS/MS search and retention time matching.
Course participants will learn about existing MS/MS databases and free and commercial program solutions for MS/MS search as well as compound identifications strategies for high-resolution tandem mass spectral data. Participants will expand the knowledge about the structure and data formats of LC-MS/MS files (mzXML, mzML) and tandem mass spectra (MGF) and learn how to convert and manipulate such data.
The hands-on training will focus on real-world data sets that were acquired with QTOF-MS/MS. Annotation strategies using accurate mass precursor lookup and accurate mass product ion search will be compared and integrated. The concepts of mass spectral scoring and the impact of different search scoring algorithms will be discussed.
The course will allow participants to readily apply the learned objectives to their own datasets and diverse instrumental setups.
The data analysis and interpretation section will cover major topics in statistical analysis, multivariate methods and biochemical pathway mapping. Course participants will be exposed to common approaches for the analysis of high dimensional biological data sets using freely available software and real-world metabolomic data sets.
A combination of presentations, case studies and hands-on analyses will expose the participant to a variety of statistical approaches including multiple hypothesis testing, false discovery rate adjustment, analysis of correlations and power analysis.
Hands on labs will be used to introduce and gain experience using common multivariate data analysis techniques including: clustering, principal component analysis (PCA), partial least squares projection to latent structures (PLS/PLS-DA), multivariate modeling and feature selection. Biological interpretation of statistical and multivariate results will be enhanced using metabolite over representation (ORA) and pathway enrichment analysis (PEA).
The participants will be introduced to the concept of network mapping and get hands on training in the generation of biochemical, structural and mass spectral similarity networks. Overall emphasis will be placed on data visualization and interpretation of results within biological contexts.
More information and updates may be found on the Metabolomics Core website.
*If choosing to pay by credit card, participants must do so here.