Open-Source Sequence Clustering Methods Improve the State Of the Art

Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).