CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization

The recent nucleic acid sequencing revolution driven by shotgun and high-throughput technologies has led to a rapid increase in the number of sequences for microbial communities. The availability of 16S ribosomal RNA (rRNA) gene sequences from a multitude of natural environments now offers a unique opportunity to study microbial diversity and community structure. The large volume of sequencing data however makes it time consuming to assign individual sequences to phylotypes by searching them against public databases. Since ribosomal sequences have diverged across prokaryotic species, they can be grouped into clusters that represent operational taxonomic units. However, available clustering programs suffer from overlap of sequence spaces in adjacent clusters. In natural environments, gene sequences are homogenous within species but divergent between species. This evolutionary constraint results in an uneven distribution of genetic distances of genes in sequence space. To cluster 16S rRNA sequences more accurately, it is therefore essential to select core sequences that are located at the centers of the distributions represented by the genetic distance of sequences in taxonomic units. Based on this idea, we here describe a novel sequence clustering algorithm named CLUSTOM that minimizes the overlaps between adjacent clusters. The performance of this algorithm was evaluated in a comparative exercise with existing programs, using the reference sequences of the SILVA database as well as published pyrosequencing datasets. The test revealed that our algorithm achieves higher accuracy than ESPRIT-Tree and mothur, few of the bes