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Integration of Metabolomics and Transcriptomics to Identify Gene-Metabolite Relationships Specific to Phenotype

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Cancer Metabolism

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1928))

Abstract

Metabolomics plays an increasingly large role in translational research, with metabolomics data being generated in large cohorts, alongside other omics data such as gene expression. With this in mind, we provide a review of current approaches that integrate metabolomic and transcriptomic data. Furthermore, we provide a detailed framework for integrating metabolomic and transcriptomic data using a two-step approach: (1) numerical integration of gene and metabolite levels to identify phenotype (e.g., cancer)-specific gene-metabolite relationships using IntLIM and (2) knowledge-based integration, using pathway overrepresentation analysis through RaMP, a comprehensive database of biological pathways. Each step makes use of publicly available R packages (https://github.com/mathelab/IntLIM and https://github.com/mathelab/RaMP-DB), and provides a user-friendly web interface for analysis. These interfaces can be run locally through the package or can be accessed through our servers (https://intlim.bmi.osumc.edu and https://ramp-db.bmi.osumc.edu). The goal of this chapter is to provide step-by-step instructions on how to install the software and use the commands within the R framework, without the user interface (which is slower than running the commands through command line). Both packages are in continuous development so please refer to the GitHub sites to check for updates.

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Acknowledgment

This work was supported by funding from the National Cancer Institute (1R03CA222428-01) and the Ohio State University Translational Data Analytics Institute and startup funds by the Ohio State University to Ewy Mathé, by the Ohio State University Discovery Themes Foods for Health postdoctoral fellowship to Jalal Siddiqui, and by the National Institute of General Medical Sciences of the National Institutes of Health to Andy Patt (T32GM068412). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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  1. The Ohio State University College of Medicine, Columbus, OH, USA

    Andrew Patt, Jalal Siddiqui, Bofei Zhang & Ewy Mathé

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  1. Andrew Patt
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  2. Jalal Siddiqui
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  4. Ewy Mathé
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Correspondence to Ewy Mathé .

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  1. National Cancer Institute, North Bethesda, MD, USA

    Majda Haznadar

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Patt, A., Siddiqui, J., Zhang, B., Mathé, E. (2019). Integration of Metabolomics and Transcriptomics to Identify Gene-Metabolite Relationships Specific to Phenotype. In: Haznadar, M. (eds) Cancer Metabolism. Methods in Molecular Biology, vol 1928. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9027-6_23

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  • DOI: https://doi.org/10.1007/978-1-4939-9027-6_23

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