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

Authors

• David J. Russell, University of Nebraska-LincolnFollow
• Samuel F. Way, University of Nebraska-Lincoln
• Andrew K. Benson, University of Nebraska-LincolnFollow
• Khalid Sayood, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

2010

Citation

BMC Bioinformatics 2010, 11:601

Comments

Copyright 2010 Russell et al; licensee BioMed Central Ltd

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.

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.