Bioinformatics Tools for the Interpretation of Metabolomics Data

Luiz Gustavo Gardinassi, Jianguo Xia, Sandra E. Safo, Shuzhao Li

Research output: Contribution to journalReview articlepeer-review

30 Scopus citations


Purpose of Review: Metabolomics is a rapidly evolving field that generates large and complex datasets. Bioinformatics becomes critical towards the extraction of meaningful biological information. In this article, we briefly review computational approaches that have been well accepted in the field, and discuss the development of new methods and tools to interpret metabolomics data. Recent Findings: Significant progress has been made in computational metabolomics over the past years. This includes methods that are used to preprocess data generated by instruments, to annotate metabolites, to carry out statistical analyses, to identify perturbed metabolic pathways, and to integrate metabolomics with other omics data. Each of these topics is discussed in respective sections of this review. Summary: Bioinformatics tools used for metabolomics remain a highly active research area. An ecosystem is emerging with software libraries, standalone tools, and web-based tools and services. While some require bioinformatics training, many of them are user friendly and easily accessible. Much further development is still needed to serve the metabolomics field and its applications.

Original languageEnglish (US)
Pages (from-to)374-383
Number of pages10
JournalCurrent Pharmacology Reports
Issue number6
StatePublished - Dec 1 2017

Bibliographical note

Funding Information:
Acknowledgments We thank Cecilia Villaveces for editorial assistance. Funding Information This work was supported by National Institutes of Health (NIAID HHSN272201200031C, HHSN272201300018I, UH2AI132345, NIEHS P30 ES019776, U2CES026560, P50ES026071).

Publisher Copyright:
© 2017, Springer International Publishing AG.


  • Bioinformatics
  • Data analysis
  • Data integration
  • Metabolic pathways
  • Metabolomics


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