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We propose a sequence clustering algorithm and compare the partition quality and execution time of the proposed algorithm with those of a popular existing algorithm. The proposed clustering algorithm uses a grammar-based distance metric to determine partitioning for a set of biological sequences. The algorithm performs clustering in which new sequences are compared with cluster-representative sequences to determine membership. If comparison fails to identify a suitable cluster, a new cluster isdoi:10.1186/1471-2105-11-601 pmid:21167044 pmcid:PMC3022630 fatcat:r42i322w45hyjgaurxw3zpy7km