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Using the IDEOM Workflow for LCMS-Based Metabolomics Studies of Drug Mechanisms

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Book cover Computational Methods and Data Analysis for Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2104))

Abstract

Rapid advancements in metabolomics technologies have allowed for application of liquid chromatography mass spectrometry (LCMS)-based metabolomics to investigate a wide range of biological questions. In addition to an important role in studies of cellular biochemistry and biomarker discovery, an exciting application of metabolomics is the elucidation of mechanisms of drug action (Creek et al., Antimicrob Agents Chemother 60:6650–6663, 2016; Allman et al., Antimicrob Agents Chemother 60:6635–6649, 2016). Although it is a very useful technique, challenges in raw data processing, extracting useful information out of large noisy datasets, and identifying metabolites with confidence, have meant that metabolomics is still perceived as a highly specialized technology. As a result, metabolomics has not yet achieved the anticipated extent of uptake in laboratories around the world as genomics or transcriptomics. With a view to bring metabolomics within reach of a nonspecialist scientist, here we describe a routine workflow with IDEOM, which is a graphical user interface within Microsoft Excel, which almost all researchers are familiar with. IDEOM consists of custom built algorithms that allow LCMS data processing, automatic noise filtering and identification of metabolite features (Creek et al., Bioinformatics 28:1048–1049, 2012). Its automated interface incorporates advanced LCMS data processing tools, mzMatch and XCMS, and requires R for complete functionality. IDEOM is freely available for all researchers and this chapter will focus on describing the IDEOM workflow for the nonspecialist researcher in the context of studies designed to elucidate mechanisms of drug action.

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Authors and Affiliations

  1. Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia

    Anubhav Srivastava & Darren J. Creek

Authors
  1. Anubhav Srivastava
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  2. Darren J. Creek
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Corresponding author

Correspondence to Darren J. Creek .

Editor information

Editors and Affiliations

  1. Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Shuzhao Li

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Srivastava, A., Creek, D.J. (2020). Using the IDEOM Workflow for LCMS-Based Metabolomics Studies of Drug Mechanisms. In: Li, S. (eds) Computational Methods and Data Analysis for Metabolomics. Methods in Molecular Biology, vol 2104. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0239-3_21

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  • DOI: https://doi.org/10.1007/978-1-0716-0239-3_21

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0238-6

  • Online ISBN: 978-1-0716-0239-3

  • eBook Packages: Springer Protocols

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