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Metabolomic data streaming for biology-dependent data acquisition

Nature Biotechnology volume 32, pages 524–527 (2014)Cite this article

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To the Editor:

Over the past 10 years, metabolomics has emerged as a powerful technology to interrogate cellular biochemistry at the global level. Although much of the success has been driven by advances in mass spectrometry, developments in bioinformatic resources for data processing have been equally important. The widely used metabolomic software XCMS, in particular, has undergone substantial improvements since its introduction in 2005 (ref. 1). In addition to improved algorithms for peak picking, retention-time alignment and data visualization, XCMS has transitioned from a command-line interface, requiring expertise in the R programming language, to a web-based platform with a graphical user interface2. This web-based platform, called XCMS Online, enables thousands of users to upload their metabolomic data and perform cloud-based processing.

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Figure 1: Time saved by metabolomic data streaming.
Figure 2: Biology-dependent data acquisition from tumor samples.

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Acknowledgements

This work was supported by the US National Institutes of Health grants R01 CA170737 (G.S.), R24 EY017540 (G.S.), P30 MH062261 (G.S.), RC1 HL101034 (G.S.), P01 DA026146 (G.S.) and R01 ES022181 (G.J.P.) and the US National Institutes of Health National Institute on Aging grant L30 AG0 038036 (G.J.P.). Financial support was also received from the US Department of Energy grants FG02-07ER64325 and DE-AC0205CH11231 (G.S.).

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Author notes

  1. Duane Rinehart and Caroline H Johnson: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Chemistry, The Scripps Research Institute, La Jolla, California, USA

    Duane Rinehart, Caroline H Johnson, Thomas Nguyen, Julijana Ivanisevic, H Paul Benton & Gary Siuzdak

  2. Department of Cell and Molecular Biology, The Scripps Research Institute, La Jolla, California, USA

    Duane Rinehart, Caroline H Johnson, Thomas Nguyen, Julijana Ivanisevic, H Paul Benton & Gary Siuzdak

  3. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA

    Duane Rinehart, Caroline H Johnson, Thomas Nguyen, Julijana Ivanisevic, H Paul Benton & Gary Siuzdak

  4. Center for Metabolomics, The Scripps Research Institute, La Jolla, California, USA

    Duane Rinehart, Caroline H Johnson, Thomas Nguyen, Julijana Ivanisevic, H Paul Benton & Gary Siuzdak

  5. Department of Chemistry, Washington University, St. Louis, Missouri, USA

    Jessica Lloyd & Gary J Patti

  6. Department of Genetics, Washington University, St. Louis, Missouri, USA

    Jessica Lloyd & Gary J Patti

  7. Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA

    Jessica Lloyd & Gary J Patti

  8. Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Adam P Arkin & Adam M Deutschbauer

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  1. Duane Rinehart
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Corresponding authors

Correspondence to Gary J Patti or Gary Siuzdak.

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Supplementary Figures 1–4 and Supplementary Software 1 and 2 (PDF 4538 kb)

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Rinehart, D., Johnson, C., Nguyen, T. et al. Metabolomic data streaming for biology-dependent data acquisition. Nat Biotechnol 32, 524–527 (2014). https://doi.org/10.1038/nbt.2927

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