Abstract
De Brujin graphs are widely used in bioinformatics for processing next-generation sequencing (NGS) data. Due to the very large size of NGS datasets, it is essential to represent de Bruijn graphs compactly, and several approaches to this problem have been proposed recently. In this work, we show how to reduce the memory required by the algorithm of Chikhi and Rizk (WABI, 2012) that represents de Brujin graphs using Bloom filters. Our method requires 30% to 40% less memory with respect to their method, with insignificant impact to construction time. At the same time, our experiments showed a better query time compared to their method. This is, to our knowledge, the best practical representation for de Bruijn graphs.
Keywords
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blattner, F.R., Plunkett, G., Bloch, C.A., et al.: The complete genome sequence of Escherichia coli k-12. Science 277(5331), 1453–1462 (1997)
Bowe, A., Onodera, T., Sadakane, K., Shibuya, T.: Succinct de Bruijn graphs. In: Raphael, B., Tang, J. (eds.) WABI 2012. LNCS, vol. 7534, pp. 225–235. Springer, Heidelberg (2012)
Chikhi, R., Rizk, G.: Space-efficient and exact de bruijn graph representation based on a bloom filter. In: Raphael, B., Tang, J. (eds.) WABI 2012. LNCS, vol. 7534, pp. 236–248. Springer, Heidelberg (2012)
Conway, T.C., Bromage, A.J.: Succinct data structures for assembling large genomes. Bioinformatics 27(4), 479–486 (2011)
Grabherr, M.G., Haas, B.J., Yassour, M., Levin, J.Z., et al.: Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotech. 29(7), 644–652 (2011)
Iqbal, Z., Caccamo, M., Turner, I., Flicek, P., McVean, G.: De novo assembly and genotyping of variants using colored de Bruijn graphs. Nat. Genet. 44(2), 226–232 (2012)
Kirsch, A., Mitzenmacher, M.: Less hashing, same performance: Building a better bloom filter. Random Struct. Algorithms 33(2), 187–218 (2008)
Miller, J.R., Koren, S., Sutton, G.: Assembly algorithms for next-generation sequencing data. Genomics 95(6), 315–327 (2010)
Pell, J., Hintze, A., Canino-Koning, R., Howe, A., Tiedje, J.M., Brown, C.T.: Scaling metagenome sequence assembly with probabilistic de Bruijn graphs. Proc. Natl. Acad. Sci. U.S.A. 109(33), 13272–13277 (2012)
Peng, Y., Leung, H.C.M., Yiu, S.M., Chin, F.Y.L.: Meta-IDBA: a de novo assembler for metagenomic data. Bioinformatics 27(13), i94–i101 (2011)
Pevzner, P.A., Tang, H., Waterman, M.S.: An Eulerian path approach to DNA fragment assembly. Proc. Natl. Acad. Sci. U.S.A. 98(17), 9748–9753 (2001)
Porat, E.: An optimal Bloom filter replacement based on matrix solving. In: Frid, A., Morozov, A., Rybalchenko, A., Wagner, K.W. (eds.) CSR 2009. LNCS, vol. 5675, pp. 263–273. Springer, Heidelberg (2009)
Rizk, G., Lavenier, D., Chikhi, R.: DSK: k-mer counting with very low memory usage. Bioinformatics (2013)
Sacomoto, G., Kielbassa, J., Chikhi, R., Uricaru, R., et al.: KISSPLICE: de-novo calling alternative splicing events from RNA-seq data. BMC Bioinformatics 13(suppl. 6), S5 (2012)
Ye, C., Ma, Z., Cannon, C., Pop, M., Yu, D.: Exploiting sparseness in de novo genome assembly. BMC Bioinformatics 13(suppl. 6), S1 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salikhov, K., Sacomoto, G., Kucherov, G. (2013). Using Cascading Bloom Filters to Improve the Memory Usage for de Brujin Graphs. In: Darling, A., Stoye, J. (eds) Algorithms in Bioinformatics. WABI 2013. Lecture Notes in Computer Science(), vol 8126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40453-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-40453-5_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40452-8
Online ISBN: 978-3-642-40453-5
eBook Packages: Computer ScienceComputer Science (R0)