Abstract
BackgroundOne of the most crucial steps in high-throughput sequence-based microbiome studies is the taxonomic assignment of sequences belonging to operational taxonomic units (OTUs). Without taxonomic classification, functional and biological information of microbial communities cannot be inferred or interpreted. The internal transcribed spacer (ITS) region of the ribosomal DNA is the conventional marker region for fungal community studies. While bioinformatics pipelines that cluster reads into OTUs have received much attention in the literature, less attention has been given to the taxonomic classification of these sequences, upon which biological inference is dependent.ResultsHere we compare how three common fungal OTU taxonomic assignment tools (RDP Classifier, UTAX, and SINTAX) handle ITS fungal sequence data. The classification power, defined as the proportion of assigned OTUs at a given taxonomic rank, varied among the classifiers. Classifiers were generally consistent (assignment of the same taxonomy to a given OTU) across datasets and ranks; a small number of OTUs were assigned unique classifications across programs. We developed CONSTAX (CONSensus TAXonomy), a Python tool that compares taxonomic classifications of the three programs and merges them into an improved consensus taxonomy. This tool also produces summary classification outputs that are useful for downstream analyses.ConclusionsOur results demonstrate that independent taxonomy assignment tools classify unique members of the fungal community, and greater classification power is realized by generating consensus taxonomy of available classifiers with CONSTAX.
Highlights
One of the most crucial steps in high-throughput sequence-based microbiome studies is the taxonomic assignment of sequences belonging to operational taxonomic units (OTUs)
Pipelines for processing fungal internal transcribed spacer (ITS) amplicon datasets such as CLOTU [11], CloVR-ITS [12], PIPITS [1], and others [13] are available in the literature, but most of the tool-development effort has been towards generating nearly automated pipelines for filtering, trimming, and clustering of amplicon reads into operational taxonomic units (OTUs)
Power of classifiers Classification power differed across RDP Classifier (RDPC), UTAX, and SINTAX (Fig. 2)
Summary
One of the most crucial steps in high-throughput sequence-based microbiome studies is the taxonomic assignment of sequences belonging to operational taxonomic units (OTUs). While bioinformatics pipelines that cluster reads into OTUs have received much attention in the literature, less attention has been given to the taxonomic classification of these sequences, upon which biological inference is dependent. High-throughput sequencing of DNA barcode marker regions, namely the bacterial 16S rRNA gene or fungal internal transcribed spacer (ITS) ribosomal regions, have allowed researchers to characterize complex microbial communities at a depth not previously possible with culture-based methods. There are a variety of algorithms to use for the taxonomy assignment step, which include: BLAST [17], Ribosomal Database Project (RDP) Naïve Bayesian Classifier [18], UTAX [19], and SINTAX [20]. Use of BLAST to identify OTUs in amplicon-based microbiome datasets has low accuracy as demonstrated previously [20,21,22], and discussed by Wang et al [18]
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