A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences

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Abstract

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 is created.

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Russell, D. J., Way, S. F., Benson, A. K., & Sayood, K. (2010). A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences. BMC Bioinformatics, 11. https://doi.org/10.1186/1471-2105-11-601

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