Introduction

Functional metagenomic analyses commonly involve a normalization step, where measured levels of genes or pathways are converted into relative abundances. Here, we demonstrate that this normalization scheme introduces marked biases both across and within human microbiome samples, and identify sample- and gene-specific properties that contribute to these biases. We introduce an alternative normalization paradigm, MUSiCC, which combines universal single-copy genes with machine learning methods to correct these biases and to obtain an accurate and biologically meaningful measure of gene abundances. Finally, we demonstrate that MUSiCC significantly improves downstream discovery of functional shifts in the microbiome.MUSiCC is available at http://elbo.gs.washington.edu/software.html .

Publications

  1. MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome.
    Cite this
    Manor O, Borenstein E, 2015-03-01 - Genome biology

Credits

  1. Ohad Manor
    Developer

    Department of Genome Sciences, University of Washington, United States of America

  2. Elhanan Borenstein
    Investigator

    Santa Fe Institute, Santa Fe

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Summary
AccessionBT006994
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByElhanan Borenstein