title: MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome.
abstract: 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 .
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. ohadm@uw.edu.
authors.forename: Ohad
authors.initials: O
authors.lastname: Manor
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. elbo@uw.edu.|Department of Computer Science and Engineering, University of Washington, Seattle, WA, 98195, USA. elbo@uw.edu.|Santa Fe Institute, Santa Fe, NM, 87501, USA. elbo@uw.edu.
authors.forename: Elhanan
authors.initials: E
authors.lastname: Borenstein
mesh_terms: D000465:Algorithms; D049109:Cell Proliferation / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D000069550:Machine Learning; D056186:Metagenomics; D064307:Microbiota / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*