title: Metagenomic profiling of known and unknown microbes with microbeGPS.
abstract: Microbial community profiling identifies and quantifies organisms in metagenomic sequencing data using either reference based or unsupervised approaches. However, current reference based profiling methods only report the presence and abundance of single reference genomes that are available in databases. Since only a small fraction of environmental genomes is represented in genomic databases, these approaches entail the risk of false identifications and often suggest a higher precision than justified by the data. Therefore, we developed MicrobeGPS, a novel metagenomic profiling approach that overcomes these limitations. MicrobeGPS is the first method that identifies microbiota in the sample and estimates their genomic distances to known reference genomes. With this strategy, MicrobeGPS identifies organisms down to the strain level and highlights possibly inaccurate identifications when the correct reference genome is missing. We demonstrate on three metagenomic datasets with different origin that our approach successfully avoids misleading interpretation of results and additionally provides more accurate results than current profiling methods. Our results indicate that MicrobeGPS can enable reference based taxonomic profiling of complex and less characterized microbial communities. MicrobeGPS is open source and available from https://sourceforge.net/projects/microbegps/ as source code and binary distribution for Windows and Linux operating systems.
authors: Research Group Bioinformatics (NG4), Robert Koch Institute, Berlin, Germany.
authors: Martin S
authors: MS
authors: Lindner
authors: Research Group Bioinformatics (NG4), Robert Koch Institute, Berlin, Germany.
authors: Bernhard Y
authors: BY
authors: Renard
mesh_terms: D005845:Georgia; D006801:Humans; D007422:Intestines / Q000382:microbiology; D060106:Lakes / Q000382:microbiology; D056186:Metagenomics / Q000379:methods* / Q000592:standards; D008829:Microbiology; D012015:Reference Standards
references: Appl Environ Microbiol. 2011 Sep;77(17):6000-11
references: 21764968
references: Nucleic Acids Res. 2013 Jan 7;41(1):e10
references: 22941661
references: Genome Biol. 2014 Mar 03;15(3):R46
references: 24580807
references: Nat Methods. 2012 Mar 04;9(4):357-9
references: 22388286
references: ISME J. 2012 Sep;6(9):1715-27
references: 22717885
references: PLoS One. 2014 Apr 08;9(4):e93827
references: 24714158
references: Bioinformatics. 2011 Jun 15;27(12):1618-24
references: 21546400
references: Genome Res. 2013 Oct;23(10):1721-9
references: 23843222
references: Proc Natl Acad Sci U S A. 2005 Feb 15;102(7):2567-72
references: 15701695
references: Nat Rev Genet. 2013 Mar;14(3):157-67
references: 23358380
references: Nat Methods. 2012 Jun 10;9(8):811-4
references: 22688413
references: Bioinformatics. 2013 May 15;29(10):1260-7
references: 23589648
references: Psychometrika. 1967 Sep;32(3):241-54
references: 5234703
references: Nat Rev Genet. 2010 Jan;11(1):31-46
references: 19997069
references: Nature. 2012 Jun 13;486(7402):215-21
references: 22699610
references: Nucleic Acids Res. 2012 Jan;40(Database issue):D130-5
references: 22121212
references: Nat Methods. 2013 Dec;10(12):1196-9
references: 24141494
references: Appl Environ Microbiol. 2011 Feb;77(4):1153-61
references: 21169428
references: Science. 2007 Feb 23;315(5815):1126-30
references: 17272687
references: Science. 2010 May 21;328(5981):994-9
references: 20489017
references: PLoS One. 2013 Sep 03;8(9):e73071
references: 24019895
references: J Comput Biol. 2011 Mar;18(3):523-34
references: 21385052
references: Nat Commun. 2013;4:1854
references: 23673639
references: Nucleic Acids Res. 2013 Apr;41(7):e78
references: 23358824
references: Bioinformatics. 2013 Nov 1;29(21):2790-1
references: 23975764
references: Genome Res. 2007 Mar;17(3):377-86
references: 17255551
references: JAMA. 2013 Apr 10;309(14):1502-10
references: 23571589