TACO: Taxonomic prediction of unknown OTUs through OTU co-abundance networks

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.

Graphical abstract

Keywords

metagenomics / 16s rRNA gene / taxonomic profiling / taxonomic prediction / Markov Random Field / OTU co-abundance network

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at DOI 10.1007/s40484-016-0073-2.

ACKNOWLEDGEMENTS

The authors thank Professor Fengzhu Sun for his helpful suggestions. This research was partially supported by NIH Center of Excellence in Genomic Sciences (NIH/HG 2 P50 HG002790-06), NIH/NHGRI 1U01 HG006531-01, NSF/DMS ATD 7031026, and NSFC 91019016.

COMPLIANCE WITH ETHICS GUIDELINES

The author Zohreh Baharvand Irannia andTing Chen declare that they have no conflict of interests.

Funding

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg