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Identifying Differentially Abundant Metabolic Pathways in Metagenomic Datasets

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Bioinformatics Research and Applications (ISBRA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6053))

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Abstract

Enabled by rapid advances in sequencing technology, metagenomic studies aim to characterize entire communities of microbes bypassing the need for culturing individual bacterial members. One major goal of such studies is to identify specific functional adaptations of microbial communities to their habitats. Here we describe a powerful analytical method (MetaPath) that can identify differentially abundant pathways in metagenomic data-sets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge. We show that MetaPath outperforms other common approaches when evaluated on simulated datasets. We also demonstrate the power of our methods in analyzing two, publicly available, metagenomic datasets: a comparison of the gut microbiome of obese and lean twins; and a comparison of the gut microbiome of infant and adult subjects. We demonstrate that the subpathways identified by our method provide valuable insights into the biological activities of the microbiome.

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Authors and Affiliations

  1. Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, Department of Computer Science, Univeristy of Maryland, College Park, USA

    Bo Liu & Mihai Pop

Authors
  1. Bo Liu
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  2. Mihai Pop
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Editor information

Editors and Affiliations

  1. School of Biology, Department of Biomedical Engineering and College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA

    Mark Borodovsky

  2. Molecular and Cell Biology Department, University of Connecticut, 91 North Eagleville Road, Unit 3125, 06269-3125, Storrs, CT, USA

    Johann Peter Gogarten

  3. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Building 38A 8S814, 20894, Bethesda, MD, USA

    Teresa M. Przytycka

  4. Department of Computer Science and Engineering, University of Connecticut, 257 ITE Building, 371 Fairfield Way, 06269-2155, Storrs, CT, USA

    Sanguthevar Rajasekaran

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Liu, B., Pop, M. (2010). Identifying Differentially Abundant Metabolic Pathways in Metagenomic Datasets. In: Borodovsky, M., Gogarten, J.P., Przytycka, T.M., Rajasekaran, S. (eds) Bioinformatics Research and Applications. ISBRA 2010. Lecture Notes in Computer Science(), vol 6053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13078-6_12

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  • DOI: https://doi.org/10.1007/978-3-642-13078-6_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13077-9

  • Online ISBN: 978-3-642-13078-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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