title: Bayesian mixture analysis for metagenomic community profiling.
abstract: BACKGROUND
  Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture.
  
  
  RESULTS
  We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures.
  
  
  METHODS
  metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix
  
  
  BACKGROUND
  sofia.morfopoulou.10@ucl.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bionformatics online.
authors.affiliation: UCL Genetics Institute, University College London, London WC1E 6BT, UK.
authors.forename: Sofia
authors.initials: S
authors.lastname: Morfopoulou
authors.affiliation: UCL Genetics Institute, University College London, London WC1E 6BT, UK.
authors.forename: Vincent
authors.initials: V
authors.lastname: Plagnol
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D055877:Biosurveillance; D019295:Computational Biology / Q000379:methods*; D004269:DNA, Bacterial / Q000235:genetics / Q000302:isolation & purification; D004279:DNA, Viral / Q000235:genetics / Q000302:isolation & purification; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D008390:Markov Chains; D056186:Metagenomics / Q000379:methods*; D051379:Mice; D009010:Monte Carlo Method; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
chemical_list: D004269:DNA, Bacterial; D004279:DNA, Viral