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

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.