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Computational Strategies for Biological Interpretation of Metabolomics Data

  • Jianguo XiaEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 965)

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.

Keywords

Metabolomics Chemometrics Metabolic profiling Metabolite set enrichment analysis Chemo-enrichment analysis 

Abbreviations

AMDIS

Automated mass spectral deconvolution and identification system

BATMAN

Bayesian automated metabolite analyzer for NMR

GC-MS

Gas chromatography mass spectrometry

CSF

Cerebral spinal fluid

GO

Gene ontology

GSEA

Gene set enrichment analysis

LC-MS

Liquid chromatography mass spectrometry

MSEA

Metabolite set enrichment analysis

NIST

National Institute of Standards and Technology

NMR

Nuclear magnetic resonance

PCA

Principal component analysis

PLS-DA

Partial least squares discriminant analysis

OPLS-DA

Orthogonal partial least squares discriminant analysis

ORA

Overrepresentation analysis

PCR

Polymerase chain reaction

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Parasitology, and Department of Animal ScienceMcGill UniversitySainte Anne de BellevueCanada
  2. 2.Department of Microbiology and ImmunologyMcGill UniversityMontrealCanada

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