title: Computational Strategies for Biological Interpretation of Metabolomics Data.
abstract: Biological interpretation of metabolomics data relies on two basic steps: metabolite identification and functional analysis. These two steps need to be applied in a coordinated manner to enable effective data understanding. The focus of this chapter is to introduce the main computational concepts and workflows during this process. After a general overview of the field, three sections will be presented: the first section will introduce the main computational methods and bioinformatics tools for metabolite identification using spectra from common analytical platforms; the second section will focus on introducing major bioinformatics approaches for functional enrichment analysis of metabolomics data; and the last section will discuss the three main workflows in current metabolomics studies, including the chemometrics approach, the metabolic profiling approach and the more recent chemo-enrichment analysis approach. The chapter ends with summary and future perspectives on computational metabolomics.
authors.affiliation: Institute of Parasitology, and Department of Animal Science, McGill University, Sainte Anne de Bellevue, QC, H9X 3V9, Canada. jeff.xia@mcgill.ca.|Department of Microbiology and Immunology, McGill University, Montreal, QC, H3A 2B4, Canada. jeff.xia@mcgill.ca.
authors.forename: Jianguo
authors.initials: J
authors.lastname: Xia
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods*
keywords: Chemo-enrichment analysis; Chemometrics; Metabolic profiling; Metabolite set enrichment analysis; Metabolomics