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Quantitative Biology

ISSN 2095-4689

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Quant. Biol.    2016, Vol. 4 Issue (3) : 149-158    https://doi.org/10.1007/s40484-016-0073-2
RESEARCH ARTICLE
TACO: Taxonomic prediction of unknown OTUs through OTU co-abundance networks
Zohreh Baharvand Irannia1,Ting Chen1,2()
1. Program in Computational Biology and Bioinformatics, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
2. Bioinformatics Division, TNLIST, Tsinghua University, Beijing 100084, China
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Abstract

Background: A main goal of metagenomics is taxonomic characterization of microbial communities. Although sequence comparison has been the main method for the taxonomic classification, there is not a clear agreement on similarity calculation and similarity thresholds, especially at higher taxonomic levels such as phylum and class. Thus taxonomic classification of novel metagenomic sequences without close homologs in the biological databases poses a challenge.

Methods: In this study, we propose to use the co-abundant associations between taxa/operational taxonomic units (OTU) across complex and diverse communities to assist taxonomic classification. We developed a Markov Random Field model to predict taxa of unknown microorganisms using co-abundant associations.

Results: Although such associations are intrinsically functional associations, we demonstrate that they are strongly correlated with taxonomic associations and can be combined with sequence comparison methods to predict taxonomic origins of unknown microorganisms at phylum and class levels.

Conclusions: With the ever-increasing accumulation of sequence data from microbial communities, we now take the first step to explore these associations for taxonomic identification beyond sequence similarity.

Availability and Implementation: Source codes of TACO are freely available at the following URL: https://github.com/baharvand/OTU-Taxonomy-Identification implemented in C++, supported on Linux and MS Windows.
Author Summary   

This paper proposes a new approach to taxonomic classification of novel metagenomic sequences. Combining sequence similarity information with co-abundant associations between taxa/operational taxonomic units (OTU) across complex and diverse communities, we develop a statistical model to predict taxonomic origins of unknown microorganisms at phylum and class levels. The results demonstrate that OTU co-abundant associations are strongly correlated with taxonomic associations.
Keywords metagenomics      16s rRNA gene      taxonomic profiling      taxonomic prediction      Markov Random Field      OTU co-abundance network     
PACS:     
Fund: 
Corresponding Author(s): Ting Chen   
Just Accepted Date: 01 June 2016   Online First Date: 30 June 2016    Issue Date: 07 September 2016
 Cite this article:   
Zohreh Baharvand Irannia,Ting Chen. TACO: Taxonomic prediction of unknown OTUs through OTU co-abundance networks[J]. Quant. Biol., 2016, 4(3): 149-158.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-016-0073-2
https://academic.hep.com.cn/qb/EN/Y2016/V4/I3/149
Soil Skin Intestine Soil-R Skin-R Intestine-R
Number of nodes 572 414 541 572 414 541
Number of edges 4996 3982 4000 4996 3982 4000
Avg. Degree 11 16 14 11 16 14
Clustering coefficient 0.211 0.326 0.265 0.029±0.003 0.164±0.008 0.042±0.004
Modularity 0.827 0.647 0.767 0.298±0.01 0.192±0.005 0.256±0.005
Tab.1  Network statistics for microbial co-abundance networks from soil, human skin, human intestine, and for the random networks (-R).
Class label Soil-CC Soil-RN-CC
Bacilli 0.688677 0.001027
Clostridia 0.450079 0.002208
Gammaproteobacteria 0.303088 0.00425
Alphaproteobacteria 0.125 0.001857
Deltaproteobacteria 0.105263 0.001178
Tab.2  Clustering coefficient of each class label in the soil network and the average random network.
Class label Skin-CC Skin-RN-CC
Clostridia 0.647505 0.030902
Deinococci 0.375 0
Betaproteobacteria 0.352941 0.009074
Bacteroidia 0.301282 0.017062
Actinobacteria 0.293367 0.02438
Bacilli 0.269231 0.013244
Sphingobacteria 0.172414 0.019276
Tab.3  Clustering coefficient of each class label in the human skin network and average random network.
Class label Intestine-CC Intestine-RN-CC
Clostridia 0.444464 0.0201
Negativicutes 0.377273 0.00030303
Bacteroidia 0.282927 0.006898
Actinobacteria 0.000303 0
Tab.4  Clustering coefficient for each class label in the human intestine network and the average random network.
Class labels a b -1 g- b
Bacteroidia −2.33537 −0.04245 0.247362
Actinobacteria −1.83828 −0.0435 0.198456
Clostridia −1.65726 −0.02351 0.214375
Bacilli −2.37669 −0.04131 0.261569
Betaproteobacteria −2.77259 −0.04109 0.491779
Acidobacteria_Gp4 −4.09767 0.025845 −0.09265
Sphingobacteria −2.25672 −0.02481 0.134167
Acidobacteria_Gp3 −4.32413 −0.27638 −30.2856
Flavobacteria −3.38777 0.027906 −0.95404
Epsilonproteobacteria −4.32413 −0.04917 −29.7953
Deinococci −3.61765 −0.04201 0.651011
Cyanobacteria −3.61765 −0.12877 1.07966
Chloroplast −4.32413 −0.05814 −30.0519
Deltaproteobacteria −3.49651 0.074062 −0.26545
Alphaproteobacteria −2.96527 0.051567 −0.27868
Tab.5  Estimated parameters for the human skin dataset.
Soil AUC Skin AUC Intestine AUC
Bacilli 0.76 Clostridia 0.72 Negativicutes 0.70
Betaproteobacteria 0.65 Bacteroidia 0.67 Clostridia 0.62
Deltaproteobacteria 0.59 Betaproteobacteria 0.66 Bacteroidia 0.60
Alphaproteobacteria 0.59 Deinococci 0.59 Erysipelotrichia 0.59
Gammaproteobacteria 0.59 Actinobacteria 0.56
Actinobacteria 0.54
Alphaproteobacteria 0.58
Bacilli 0.57
Tab.6  AUC values for major classes in soil, human skin, and human intestine datasets.
Fig.1  An unknown OTU (#3000) was predicted to be in the class of Bacilli according to the taxa of its neighbors in the network.
Fig.2  The neighborhood of node 9 in human intestine network has equal numbers of Bacteroidia (3) and Negativicutes (3).
Fig.3  The first and second level neighbors of node 9 in the human intestine network.

There is a high connectivity among nodes with class label Negativicutes (yellow). The probability of node 9 belonging to class Negativicutes is the highest.

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