Comparison of three clustering approaches for detecting novel environmental microbial diversity

Discovery of novel diversity in high-throughput sequencing (HTS) studies is a central task in environmental microbial ecology. To evaluate the effects that amplicon clustering methods have on novel diversity discovery, we clustered an environmental marine protist HTS dataset of protist reads together with accessions from the taxonomically curated PR 2 reference database using three de novo approaches: sequence similarity networks, USEARCH, and Swarm. The novel diversity uncovered by each clustering approach differed drastically in the number of operational taxonomic units (OTUs) and the number of environmental amplicons in these novel diversity OTUs. Global pairwise alignment comparisons revealed that numerous amplicons classified as novel by USEARCH and Swarm were actually highly similar to reference accessions. Using graph theory we found additional novel diversity within OTUs that would have gone unnoticed without further using their underlying network topologies. Our results suggest that novel diversity inferred from clustering approaches requires further validation, whereas graph theory provides a powerful tool for microbial ecology and the analyses of environmental HTS datasets.