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Bioinformatics Tools for the Interpretation of Metabolomics Data

  • Pharmacometabolomics and Toxicometabolomics (Chi Chen, Section Editor)
  • Published:
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Abstract

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.

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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).

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Authors and Affiliations

  1. Department of Medicine, Emory University School of Medicine, 615 Michael Street, Atlanta, GA, 30322-1047, USA

    Luiz Gustavo Gardinassi & Shuzhao Li

  2. Institute of Parasitology, and Department of Animal Science, McGill University, Sainte Anne de Bellevue, QC, H9X 3V9, Canada

    Jianguo Xia

  3. Department of Microbiology and Immunology, McGill University, Montreal, QC, H3A 2B4, Canada

    Jianguo Xia

  4. Division of Biostatistics, University of Minnesota, Minneapolis, MN, 55455, USA

    Sandra E Safo

Authors
  1. Luiz Gustavo Gardinassi
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  2. Jianguo Xia
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  3. Sandra E Safo
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  4. Shuzhao Li
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Correspondence to Shuzhao Li.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Pharmacometabolomics and Toxicometabolomics

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Gardinassi, L.G., Xia, J., Safo, S. et al. Bioinformatics Tools for the Interpretation of Metabolomics Data. Curr Pharmacol Rep 3, 374–383 (2017). https://doi.org/10.1007/s40495-017-0107-0

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  • DOI: https://doi.org/10.1007/s40495-017-0107-0

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