 Popular de novo Amplicon clustering methods suffer from two fundamental flaws, arbitrary global clustering thresholds, and input order dependency induced by centroid selection. SWARM was developed to address these issues by first clustering nearly identical Amplicons iteratively using a local threshold, and then by using clusters internal structure and Amplicon abundances to refine its results. This fast, scalable, and input order independent approach reduces the influence of clustering parameters and produces robust, operational taxonomic units. This article was authored by Frederick Mahe, Torbjorn Roms, Christopher Quince, and others.