Accuracy of protist diversity assessments: morphology compared to cloning                 and direct pyrosequencing of 18S rRNA genes and ITS regions using the conspicuous                 tintinnid ciliates as a case study
Deep-sequencing technologies are becoming nearly routine to describe                 microbial community composition in environmental samples. 18S rDNA pyrosequencing                 has revealed a vast diversity of infrequent sequences, leading to the proposition of                 the existence of an extremely diverse microbial "rare biosphere". While rare                 microbes no doubt exist, critical views suggest that many rare sequences may                 actually be artifacts. However, information about how diversity revealed by                 molecular methods relates to that revealed by classical morphology approaches is                 practically non-existent. To address this issue, we used different approaches to                 assess the diversity of tintinnid ciliates, a species-rich group in which species                 can be easily distinguished morphologically. We studied two Mediterranean marine                 samples with different patterns of tintinnid diversity. We estimated tintinnid                 diversity in these samples employing morphological observations as well as both                 classical cloning and sequencing and pyrosequencing of two different markers, the                 18S rDNA and the ITS regions, applying a variety of computational approaches                 currently used to analyze pyrosequence reads. We found that both molecular                 approaches were efficient in detecting the tintinnid species observed by microscopy                 and revealed similar phylogenetic structures of the tintinnid community at the                 species level. However, depending on the method used to analyze the pyrosequencing                 results, we observed discrepancies with the morphology-based assessments up to                 several orders of magnitude. In several cases, the inferred number of operational                 taxonomic units (OTUs) largely exceeded the total number of tintinnid cells in the                 samples. Such inflation of the OTU numbers corresponded to "rare biosphere" taxa,                 composed largely of artefacts. Our results suggest that a careful and rigorous                 analysis of pyrosequencing datasets, including data denoising and sequence                 clustering with well-adjusted parameters, is necessary to accurately describe                 microbial biodiversity using this molecular approach.