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)

Abstract

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.

Keywords

Binning metagenomics EM algorithm Poisson distribution 

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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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