Document Open Access Logo

Better Greedy Sequence Clustering with Fast Banded Alignment

Authors Brian Brubach, Jay Ghurye, Mihai Pop, Aravind Srinivasan

PDF

File

Thumbnail PDF
PDF
LIPIcs.WABI.2017.3.pdf
  • Filesize: 0.83 MB
  • 13 pages

Document Identifiers

Subject Classification

Keywords
  • Sequence Clustering
  • Metagenomics
  • String Algorithms

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

Comparing a string to a large set of sequences is a key subroutine in greedy heuristics for clustering genomic data. Clustering 16S rRNA gene sequences into operational taxonomic units (OTUs) is a common method used in studying microbial communities. We present a new approach to greedy clustering using a trie-like data structure and Four Russians speedup. We evaluate the running time of our method in terms of the number of comparisons it makes during clustering and show in experimental results that the number of comparisons grows linearly with the size of the dataset as opposed to the quadratic running time of other methods. We compare the clusters output by our method to the popular greedy clustering tool UCLUST. We show that the clusters we generate can be both tighter and larger.

Cite As Get BibTex

Brian Brubach, Jay Ghurye, Mihai Pop, and Aravind Srinivasan. Better Greedy Sequence Clustering with Fast Banded Alignment. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 3:1-3:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017) https://doi.org/10.4230/LIPIcs.WABI.2017.3

Author Details

Brian Brubach
Jay Ghurye
Mihai Pop
Aravind Srinivasan

References

  1. Stephen F. Altschul, Warren Gish, Webb Miller, Eugene W. Myers, and David J. Lipman. Basic local alignment search tool. Journal of molecular biology, 215(3):403-410, 1990. Google Scholar
  2. J. Gregory Caporaso, Christian L. Lauber, William A Walters, Donna Berg-Lyons, James Huntley, Noah Fierer, Sarah M. Owens, Jason Betley, Louise Fraser, Markus Bauer, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME journal, 6(8):1621-1624, 2012. Google Scholar
  3. Robert C. Edgar. Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26(19):2460-2461, 2010. Google Scholar
  4. J. Felsenstein. PHYLIP-phylogeny inference package (version 3.2). cladistics, 5:164-166, 1989. Google Scholar
  5. Mohammadreza Ghodsi, Bo Liu, and Mihai Pop. DNACLUST: accurate and efficient clustering of phylogenetic marker genes. BMC bioinformatics, 12(1):271, 2011. Google Scholar
  6. Dan Gusfield. Algorithms on strings, trees and sequences: computer science and computational biology. Cambridge university press, 1997. Google Scholar
  7. Vladimir I. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, volume 10, pages 707-710, 1966. Google Scholar
  8. Weizhong Li and Adam Godzik. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 22(13):1658-1659, 2006. Google Scholar
  9. William J. Masek and Michael S. Paterson. How to compute string-edit distances quickly. In D. Sankoff and J. B. Kruskal, editors, Time Warps, String Edits, and Macromolecules: the Theory and Practice of Sequence Comparison, pages 337-349. Addison-Wesley Publ. Co., Mass., 1983. Google Scholar
  10. William J. Masek and Mike Paterson. A faster algorithm computing string edit distances. J. Comput. Syst. Sci., 20(1):18-31, 1980. URL: http://dx.doi.org/10.1016/0022-0000(80)90002-1.
  11. Gerard Muyzer, Ellen C. De Waal, and Andre G. Uitterlinden. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Applied and environmental microbiology, 59(3):695-700, 1993. Google Scholar
  12. Eugene W. Myers. An O (ND) difference algorithm and its variations. Algorithmica, 1(1):251-266, 1986. Google Scholar
  13. Gene Myers. A fast bit-vector algorithm for approximate string matching based on dynamic programming. Journal of the ACM (JACM), 46(3):395-415, 1999. Google Scholar
  14. Temple F. Smith and Michael S. Waterman. Identification of common molecular subsequences. Journal of molecular biology, 147(1):195-197, 1981. Google Scholar
  15. Julie D. Thompson, Toby Gibson, Des G. Higgins, et al. Multiple sequence alignment using ClustalW and ClustalX. Current protocols in bioinformatics, pages 2-3, 2002. Google Scholar
  16. Lusheng Wang and Tao Jiang. On the complexity of multiple sequence alignment. Journal of computational biology, 1(4):337-348, 1994. Google Scholar
  17. James R. White, Saket Navlakha, Niranjan Nagarajan, Mohammad-Reza Ghodsi, Carl Kingsford, and Mihai Pop. Alignment and clustering of phylogenetic markers-implications for microbial diversity studies. BMC bioinformatics, 11(1):152, 2010. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact