A Novel Abundance-Based Algorithm for Binning Metagenomic Sequences Using l-Tuples

  • Yu-Wei Wu
  • Yuzhen Ye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6044)


Metagenomics is the study of microbial communities sampled directly from their natural environment, without prior culturing. Among the computational tools recently developed for metagenomic sequence analysis, binning tools attempt to classify all (or most) of the sequences in a metagenomic dataset into different bins (i.e., species), based on various DNA composition patterns (e.g., the tetramer frequencies) of various genomes. Composition-based binning methods, however, cannot be used to classify very short fragments, because of the substantial variation of DNA composition patterns within a single genome. We developed a novel approach (AbundanceBin) for metagenomics binning by utilizing the different abundances of species living in the same environment. AbundanceBin is an application of the Lander-Waterman model to metagenomics, which is based on the l-tuple content of the reads. AbundanceBin achieved accurate, unsupervised, clustering of metagenomic sequences into different bins, such that the reads classified in a bin belong to species of identical or very similar abundances in the sample. In addition, AbundanceBin gave accurate estimations of species abundances, as well as their genome sizes—two important parameters for characterizing a microbial community. We also show that AbundanceBin performed well when the sequence lengths are very short (e.g. 75 bp) or have sequencing errors.


Binning metagenomics EM algorithm Poisson distribution 


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  1. 1.
    Galperin, M.: Metagenomics: from acid mine to shining sea. Environ. Microbiol. 6, 543–545 (2004)CrossRefGoogle Scholar
  2. 2.
    Tringe, S., von Mering, C., Kobayashi, A., et al.: Comparative metagenomics of microbial communities. Science 308(5721), 554–557 (2005)CrossRefGoogle Scholar
  3. 3.
    Dinsdale, E., Pantos, O., Smriga, S., et al.: Microbial ecology of four coral atolls in the northern line islands. PLoS ONE 3(2), e158 (2008)CrossRefGoogle Scholar
  4. 4.
    Turnbaugh, P.J., Ley, R.E., Mahowald, M.A., et al.: An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444(7122), 1027–1131 (2006)CrossRefGoogle Scholar
  5. 5.
    Turnbaugh, P.J., Hamady, M., Yatsunenko, T., et al.: A core gut microbiome in obese and lean twins. Nature 457(7228), 480–484 (2009)CrossRefGoogle Scholar
  6. 6.
    Dinsdale, E.A., Edwards, R.A., Hall, D., et al.: Functional metagenomic profiling of nine biomes. Nature 452(7187), 629–632 (2008)CrossRefGoogle Scholar
  7. 7.
    Hutchison Jr., C.A.: DNA sequencing: bench to bedside and beyond. Nucleic Acids Res. 35(18), 6227–6237 (2007)CrossRefGoogle Scholar
  8. 8.
    Margulies, M., Egholm, M., Altman, W.E., et al.: Genome sequencing in microfabricated high-density picolitre reactors. Nature 437(7057), 376–380 (2005)Google Scholar
  9. 9.
    Bentley, D.R.: Whole-genome re-sequencing. Curr. Opin. Genet. Dev. 16(6), 545–552 (2006)CrossRefGoogle Scholar
  10. 10.
    Huson, D.H., Auch, A.F., Qi, J., et al.: MEGAN analysis of metagenomic data. Genome Res. 17(3), 377–386 (2007)CrossRefGoogle Scholar
  11. 11.
    Chakravorty, S., Helb, D., Burday, M., et al.: A detailed analysis of 16s ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods 69(2), 330–339 (2007)CrossRefGoogle Scholar
  12. 12.
    Monier, A., Claverie, J.M., Ogata, H.: Taxonomic distribution of large DNA viruses in the sea. Genome Biol. 9(7), R106 (2008)CrossRefGoogle Scholar
  13. 13.
    Ciccarelli, F.D., Doerks, T., von Mering, C., et al.: Toward automatic reconstruction of a highly resolved tree of life. Science 311(5765), 1283–1287 (2006)CrossRefGoogle Scholar
  14. 14.
    von Mering, C., Hugenholtz, P., Raes, J., et al.: Quantitative phylogenetic assessment of microbial communities in diverse environments. Science 315(5815), 1126–1130 (2007)CrossRefGoogle Scholar
  15. 15.
    Wu, M., Eisen, J.A.: A simple, fast, and accurate method of phylogenomic inference. Genome Biol. 9(10), 151 (2008)CrossRefGoogle Scholar
  16. 16.
    Schmidt, H.A., Strimmer, K., Vingron, M., et al.: TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. Bioinformatics 18(3), 502–504 (2002)Google Scholar
  17. 17.
    Guindon, S., Gascuel, O.: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52(5), 696–704 (2003)CrossRefGoogle Scholar
  18. 18.
    Krause, L., Diaz, N.N., Goesmann, A., et al.: Phylogenetic classification of short environmental DNA fragments. Nucleic Acids Res. 36(7), 2230–2239 (2008)Google Scholar
  19. 19.
    Finn, R.D., Mistry, J., Schuster-Bockler, B., et al.: Pfam: clans, web tools and services. Nucleic Acids Res. 34(Database issue), D247–D251 (2006)Google Scholar
  20. 20.
    Brady, A., Salzberg, S.L.: Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models. Nat. Methods 6(9), 673–676 (2009)Google Scholar
  21. 21.
    Bentley, S.D., Parkhill, J.: Comparative genomic structure of prokaryotes. Annu. Rev. Genet. 38, 771–792 (2004)CrossRefGoogle Scholar
  22. 22.
    Teeling, H., Waldmann, J., Lombardot, T., et al.: TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinformatics 5, 163 (2004)CrossRefGoogle Scholar
  23. 23.
    Woyke, T., Teeling, H., Ivanova, N.N., et al.: Symbiosis insights through metagenomic analysis of a microbial consortium. Nature 443(7114), 950–955 (2006)Google Scholar
  24. 24.
    Chatterji, S., Yamazaki, I., Bai, Z., et al.: CompostBin: A DNA composition-based algorithm for binning environmental shotgun reads. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS (LNBI), vol. 4955, pp. 17–28. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Diaz, N.N., Krause, L., Goesmann, A., et al.: TACOA: taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach. BMC Bioinformatics 10, 56 (2009)CrossRefGoogle Scholar
  26. 26.
    Zhou, F., Olman, V., Xu, Y.: Barcodes for genomes and applications. BMC Bioinformatics 9, 546 (2008)CrossRefGoogle Scholar
  27. 27.
    Foerstner, K.U., von Mering, C., Hooper, S.D., et al.: Environments shape the nucleotide composition of genomes. EMBO Rep. 6(12), 1208–1213 (2005)Google Scholar
  28. 28.
    Tyson, G.W., Chapman, J., Hugenholtz, P., et al.: Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428(6978), 37–43 (2004)CrossRefGoogle Scholar
  29. 29.
    Lander, E.S., Waterman, M.S.: Genomic mapping by fingerprinting random clones: a mathematical analysis. Genomics 2(3), 231–239 (1988)Google Scholar
  30. 30.
    Li, X., Waterman, M.S.: Estimating the repeat structure and length of DNA sequences using l-tuples. Genome Res. 13(8), 1916–1922 (2003)Google Scholar
  31. 31.
    Sharon, I., Pati, A., Markowitz, V.M., et al.: A statistical framework for the functional analysis of metagenomes. In: Batzoglou, S. (ed.) RECOMB 2009. LNCS, vol. 5541, pp. 496–511. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  32. 32.
    Richter, D.C., Ott, F., Auch, A.F., et al.: MetaSim: a sequencing simulator for genomics and metagenomics. PLoS ONE 3(10), e3373 (2008)CrossRefGoogle Scholar
  33. 33.
    Huse, S.M., Huber, J.A., Morrison, H.G., Sogin, M.L., Welch, D.M., et al.: Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol. 8(7), 143 (2007)CrossRefGoogle Scholar
  34. 34.
    White, J.R., Roberts, M., Yorke, J.A., et al.: Figaro: a novel statistical method for vector sequence removal. Bioinformatics 24(4), 462–467 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yu-Wei Wu
    • 1
  • Yuzhen Ye
    • 1
  1. 1.School of Informatics and ComputingIndiana UniversityBloomington

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