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Metabolomics: From Fundamentals to Clinical Applications

Overview of attention for book
Attention for Chapter 8: Computational Strategies for Biological Interpretation of Metabolomics Data
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Chapter title
Computational Strategies for Biological Interpretation of Metabolomics Data
Chapter number 8
Book title
Metabolomics: From Fundamentals to Clinical Applications
Published in
Advances in experimental medicine and biology, January 2017
DOI 10.1007/978-3-319-47656-8_8
Pubmed ID
Book ISBNs
978-3-31-947655-1, 978-3-31-947656-8

Jianguo Xia


Alessandra Sussulini


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.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 33%
Student > Doctoral Student 3 11%
Student > Master 3 11%
Researcher 3 11%
Lecturer 1 4%
Other 4 15%
Unknown 4 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 30%
Agricultural and Biological Sciences 7 26%
Computer Science 2 7%
Chemistry 2 7%
Immunology and Microbiology 1 4%
Other 2 7%
Unknown 5 19%