A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences |
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| Authors: | David J Russell Samuel F Way Andrew K Benson Khalid Sayood |
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| Institution: | 1.Department of Electrical Engineering,University of Nebraska-Lincoln,Lincoln,USA;2.Department of Food Science and Technology,University of Nebraska-Lincoln,Lincoln,USA;3.Core for Applied Genomics and Ecology,University of Nebraska-Lincoln,Lincoln,USA |
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| Abstract: | Background 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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