title: Metabolic network-guided binning of metagenomic sequence fragments.
abstract: Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health.
  
  
  
  We present the SOrting by NEtwork Completion (SONEC) approach for assigning reactions to incomplete metabolic networks based on a metabolite connectivity score. We successfully demonstrate proof of concept in a set of 100 genome-scale metabolic network reconstructions, and delineate the variables that impact reaction assignment accuracy. We further demonstrate the integration of SONEC with existing approaches (such as cross-sample scaffold abundance profile clustering) on a set of 94 metagenomic samples from the Human Microbiome Project. We show that not only does SONEC aid in reconstructing species-level genomes, but it also improves functional predictions made with the resulting metabolic networks.
  
  
  
  The datasets and code presented in this work are available at: https://bitbucket.org/mattbiggs/sorting_by_network_completion/
  
  
  
  papin@virginia.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22903 USA.
authors.forename: Matthew B
authors.initials: MB
authors.lastname: Biggs
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22903 USA.
authors.forename: Jason A
authors.initials: JA
authors.lastname: Papin
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D053858:Metabolic Networks and Pathways; D056186:Metagenomics; D064307:Microbiota