Bayesian estimation of bacterial community composition from 454 sequencing data

Lu Cheng, Alan W Walker, Jukka Corander

Research output: Contribution to journalArticle

19 Citations (Scopus)
3 Downloads (Pure)

Abstract

Estimating bacterial community composition from a mixed sample in different applied contexts is an important task for many microbiologists. The bacterial community composition is commonly estimated by clustering polymerase chain reaction amplified 16S rRNA gene sequences. Current taxonomy-independent clustering methods for analyzing these sequences, such as UCLUST, ESPRIT-Tree and CROP, have two limitations: (i) expert knowledge is needed, i.e. a difference cutoff between species needs to be specified; (ii) closely related species cannot be separated. The first limitation imposes a burden on the user, since considerable effort is needed to select appropriate parameters, whereas the second limitation leads to an inaccurate description of the underlying bacterial community composition. We propose a probabilistic model-based method to estimate bacterial community composition which tackles these limitations. Our method requires very little expert knowledge, where only the possible maximum number of clusters needs to be specified. Also our method demonstrates its ability to separate closely related species in two experiments, in spite of sequencing errors and individual variations.
Original languageEnglish
Pages (from-to)5240-5249
Number of pages10
JournalNucleic Acids Research
Volume40
Issue number12
Early online date9 Mar 2012
DOIs
Publication statusPublished - Jul 2012

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Cluster Analysis
Statistical Models
rRNA Genes
Polymerase Chain Reaction

Keywords

  • algorithms
  • bacteria
  • Bayes theorem
  • cluster analysis
  • models, statistical
  • phylogeny
  • sequence analysis, DNA

Cite this

Bayesian estimation of bacterial community composition from 454 sequencing data. / Cheng, Lu; Walker, Alan W; Corander, Jukka.

In: Nucleic Acids Research, Vol. 40, No. 12, 07.2012, p. 5240-5249.

Research output: Contribution to journalArticle

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