Relative Lempel-Ziv Compression of Genomes for Large-Scale Storage and Retrieval

  • Shanika Kuruppu
  • Simon J. Puglisi
  • Justin Zobel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6393)


Self-indexes – data structures that simultaneously provide fast search of and access to compressed text – are promising for genomic data but in their usual form are not able to exploit the high level of replication present in a collection of related genomes. Our ‘RLZ’ approach is to store a self-index for a base sequence and then compress every other sequence as an LZ77 encoding relative to the base. For a collection of r sequences totaling N bases, with a total of s point mutations from a base sequence of length n, this representation requires just \(nH_k(T) + s\log n + s\log \frac{N}{s} + O(s)\) bits. At the cost of negligible extra space, access to ℓ consecutive symbols requires \(\O(\ell + \log n)\) time. Our experiments show that, for example, RLZ can represent individual human genomes in around 0.1 bits per base while supporting rapid access and using relatively little memory.


Base Sequence Compression Performance Related Genome Individual Genome Query Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Apostolico, A., Lonardi, S.: Compression of biological sequences by greedy off-line textual substitution. In: Proc. IEEE DCC, pp. 143–152 (2000)Google Scholar
  2. 2.
    Cao, M.D., Dix, T., Allison, L., Mears, C.: A simple statistical algorithm for biological sequence compression. In: Proc. IEEE DCC, pp. 43–52 (2007)Google Scholar
  3. 3.
    Chen, X., Kwong, S., Li, M.: A compression algorithm for DNA sequences and its applications in genome comparison. In: Proc. RECOMB, p. 107. ACM, New York (2000)CrossRefGoogle Scholar
  4. 4.
    Christley, S., Lu, Y., Li, C., Xie, X.: Human genomes as email attachments. Bioinformatics 25(2), 274–275 (2009)CrossRefGoogle Scholar
  5. 5.
    Grumbach, S., Tahi, F.: Compression of DNA sequences. In: Proc. IEEE DCC, pp. 340–350 (1993)Google Scholar
  6. 6.
    Kuruppu, S., Beresford-Smith, B., Conway, T., Zobel, J.: Repetition-based compression of large DNA datasets. In: Poster at RECOMB (2009)Google Scholar
  7. 7.
    Mäkinen, V., Navarro, G., Sirén, J., Välimäki, N.: Storage and retrieval of highly repetitive sequence collections. J. Computational Biology 17(3), 281–308 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Navarro, G., Mäkinen, V.: Compressed full text indexes. ACM Computing Surveys 39(1) (2007)Google Scholar
  9. 9.
    Okanohara, D., Sadakane, K.: Practical entropy-compressed rank/select dictionary. In: Proc. ALENEX. SIAM, Philadelphia (2007)Google Scholar
  10. 10.
    Rivals, E., Delahaye, J., Dauchet, M., Delgrange, O.: A guaranteed compression scheme for repetitive DNA sequences. In: Proc. IEEE DCC, p. 453 (1996)Google Scholar
  11. 11.
    Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Transactions on Information Theory 23(3), 337–343 (1977)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shanika Kuruppu
    • 1
  • Simon J. Puglisi
    • 2
  • Justin Zobel
    • 1
  1. 1.National ICT Australia, Department of Computer Science & Software EngineeringUniversity of MelbourneAustralia
  2. 2.School of Computer Science and Information TechnologyRoyal Melbourne Institute of TechnologyAustralia

Personalised recommendations