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From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification

Bram Slabbinck (UGent) , Willem Waegeman (UGent) , Peter Dawyndt (UGent) , Paul De Vos (UGent) and Bernard De Baets (UGent)
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Organization
Abstract
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results: In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions: FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.
Keywords
TREE, TOOL, IDENTIFICATION, GENOMIC ERA, SPECIES DEFINITION, FATTY-ACID PROFILES, AD-HOC-COMMITTEE, FRAGMENTS

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MLA
Slabbinck, Bram et al. “From Learning Taxonomies to Phylogenetic Learning: Integration of 16S rRNA Gene Data into FAME-based Bacterial Classification.” BMC BIOINFORMATICS 11.1 (2010): n. pag. Print.
APA
Slabbinck, B., Waegeman, W., Dawyndt, P., De Vos, P., & De Baets, B. (2010). From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification. BMC BIOINFORMATICS, 11(1).
Chicago author-date
Slabbinck, Bram, Willem Waegeman, Peter Dawyndt, Paul De Vos, and Bernard De Baets. 2010. “From Learning Taxonomies to Phylogenetic Learning: Integration of 16S rRNA Gene Data into FAME-based Bacterial Classification.” Bmc Bioinformatics 11 (1).
Chicago author-date (all authors)
Slabbinck, Bram, Willem Waegeman, Peter Dawyndt, Paul De Vos, and Bernard De Baets. 2010. “From Learning Taxonomies to Phylogenetic Learning: Integration of 16S rRNA Gene Data into FAME-based Bacterial Classification.” Bmc Bioinformatics 11 (1).
Vancouver
1.
Slabbinck B, Waegeman W, Dawyndt P, De Vos P, De Baets B. From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification. BMC BIOINFORMATICS. 2010;11(1).
IEEE
[1]
B. Slabbinck, W. Waegeman, P. Dawyndt, P. De Vos, and B. De Baets, “From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification,” BMC BIOINFORMATICS, vol. 11, no. 1, 2010.
@article{846610,
  abstract     = {Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.
Results: In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model.
Conclusions: FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.},
  author       = {Slabbinck, Bram and Waegeman, Willem and Dawyndt, Peter and De Vos, Paul and De Baets, Bernard},
  issn         = {1471-2105},
  journal      = {BMC BIOINFORMATICS},
  keywords     = {TREE,TOOL,IDENTIFICATION,GENOMIC ERA,SPECIES DEFINITION,FATTY-ACID PROFILES,AD-HOC-COMMITTEE,FRAGMENTS},
  language     = {eng},
  number       = {1},
  title        = {From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification},
  url          = {http://dx.doi.org/10.1186/1471-2105-11-69},
  volume       = {11},
  year         = {2010},
}

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