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Updated: Jun 5, 2026

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization
Published on: April 14, 2016
A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences
David J Russell1, Samuel F Way, Andrew K Benson
1Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu
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.
Results:
The performance of the proposed algorithm is validated via comparison to the popular DNA/RNA sequence clustering approach, CD-HIT-EST, and to the recently developed algorithm, UCLUST, using two different sets of 16S rDNA sequences from 2,255 genera. The proposed algorithm maintains a comparable CPU execution time with that of CD-HIT-EST which is much slower than UCLUST, and has successfully generated clusters with higher statistical accuracy than both CD-HIT-EST and UCLUST. The validation results are especially striking for large datasets.
Conclusions:
We introduce a fast and accurate clustering algorithm that relies on a grammar-based sequence distance. Its statistical clustering quality is validated by clustering large datasets containing 16S rDNA sequences.
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