Abstract
Relative compression, where a set of similar strings are compressed with respect to a reference string, is an effective method of compressing DNA datasets containing multiple similar sequences. Moreover, it supports rapid random access to the underlying data. The main difficulty of relative compression is in selecting an appropriate reference sequence. In this paper, we explore using the dictionary of repeats generated by COMRAD, RE-PAIR and DNA-X algorithms as reference sequences for relative compression. We show that this technique allows for better compression, and allows more general repetitive datasets to be compressed using relative compression.
This work was supported by the Royal Society and the NICTA Victorian Research Laboratory. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Center of Excellence program.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bentley, J., McIlroy, D.: Data compression using long common strings. In: Proc. Data Compression Conference (DCC 1999), pp. 287–295 (1999)
Brandon, M., Wallace, D., Baldi, P.: Data structures and compression algorithms for genomic sequence data. Bioinformatics 25(14), 1731–1738 (2009)
Cao, M.D., Dix, T., Allison, L., Mears, C.: A simple statistical algorithm for biological sequence compression. In: Proc. Data Compression Conference (DCC 2007), pp. 43–52 (2007)
Chen, X., Li, M., Ma, B., Tromp, J.: DNACompress: fast and effective DNA sequence compression. Bioinformatics 18(12), 1696–1698 (2002)
Grabowski, S., Deorowicz, S.: Engineering relative compression of genomes (2011), http://arxiv.org/abs/1103.2351v1
Grumbach, S., Tahi, F.: A new challenge for compression algorithms: Genetic sequences. Information Processing & Management 30(6), 875–886 (1994)
Kreft, S., Navarro, G.: Self-indexing based on LZ77. In: Giancarlo, R., Manzini, G. (eds.) CPM 2011. LNCS, vol. 6661, pp. 41–54. Springer, Heidelberg (to apppear, 2011)
Kuruppu, S., Beresford-Smith, B., Conway, T., Zobel, J.: Iterative dictionary construction for compression of large DNA datasets. IEEE/ACM Transactions on Computational Biology and Bioinformatics (to appear, 2011)
Kuruppu, S., Puglisi, S.J., Zobel, J.: Relative lempel-ziv compression of genomes for large-scale storage and retrieval. In: Chavez, E., Lonardi, S. (eds.) SPIRE 2010. LNCS, vol. 6393, pp. 201–206. Springer, Heidelberg (2010)
Kuruppu, S., Puglisi, S.J., Zobel, J.: Optimized relative Lempel-Ziv compression of genomes. In: Proc. 34th Australasian Computer Science Conference (ACSC 2011), pp. 91–98 (2011)
Larsson, N.J., Moffat, A.: Offline dictionary-based compression. In: Proc. Data Compression Conference (DCC 1999), pp. 296–305 (1999)
Mäkinen, V., Navarro, G., Sirén, J., Välimäki, N.: Storage and retrieval of highly repetitive sequence collections. J. Computational Biology 17(3), 281–308 (2010)
Manzini, G., Rastero, M.: A simple and fast DNA compressor. Software: Practice and Experience 34(14), 1397–1411 (2004)
Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Transactions on Information Theory 23(3), 337–343 (1977)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kuruppu, S., Puglisi, S.J., Zobel, J. (2011). Reference Sequence Construction for Relative Compression of Genomes. In: Grossi, R., Sebastiani, F., Silvestri, F. (eds) String Processing and Information Retrieval. SPIRE 2011. Lecture Notes in Computer Science, vol 7024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24583-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-24583-1_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24582-4
Online ISBN: 978-3-642-24583-1
eBook Packages: Computer ScienceComputer Science (R0)