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Detecting Superbubbles in Assembly Graphs

  • Taku Onodera
  • Kunihiko Sadakane
  • Tetsuo Shibuya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8126)

Abstract

We introduce a new concept of a subgraph class called a superbubble for analyzing assembly graphs, and propose an efficient algorithm for detecting it. Most assembly algorithms utilize assembly graphs like the de Bruijn graph or the overlap graph constructed from reads. From these graphs, many assembly algorithms first detect simple local graph structures (motifs), such as tips and bubbles, mainly to find sequencing errors. These motifs are easy to detect, but they are sometimes too simple to deal with more complex errors. The superbubble is an extension of the bubble, which is also important for analyzing assembly graphs. Though superbubbles are much more complex than ordinary bubbles, we show that they can be efficiently enumerated. We propose an average-case linear time algorithm (i.e., O(n + m) for a graph with n vertices and m edges) for graphs with a reasonable model, though the worst-case time complexity of our algorithm is quadratic (i.e., O(n(n + m))). Moreover, the algorithm is practically very fast: Our experiments show that our algorithm runs in reasonable time with a single CPU core even against a very large graph of a whole human genome.

Keywords

Outgoing Edge Edge Label Assembly Algorithm Assembly Graph Input Vertex 
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.

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References

  1. 1.
    Batzoglou, S., Jaffe, D.B., Stanley, K., Butler, J., Gnerre, S., Mauceli, E., Berger, B., Mesirov, J.P., Lander, E.S.: Arachne: a whole-genome shotgun assembler. Genome Research 12, 177–189 (2002)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Huang, X., Yang, S.P.: Generating a genome assembly with pcap. Current Protocols in Bioinformatics, Unit 11.3 (2005)Google Scholar
  4. 4.
    Jackson, B., Regennitter, M., Yang, X., Schnable, P.S., Aluru, S.: Parallel de novo assembly of large genomes from high-throughput short reads. In: Proc. 24th International Parallel and Distributed Processing Symposium (IPDPS), pp. 1–10 (2010)Google Scholar
  5. 5.
    Kasahara, M., Morishita, S.: Large-Scale Genome Sequence Processing. Imperial College Press (2006)Google Scholar
  6. 6.
    Li, R., Zhu, H., Ruan, J., Qjan, W., Fang, X., Shi, Z., Li, Y., Li, S., Shan, G., Kristiansen, K., Yang, H., Wang, J.: De novo assembly of human genomes with massively parallel short read sequencing. Genome Research 20, 265–272 (2010)CrossRefGoogle Scholar
  7. 7.
    Lyons, R., Peres, Y.: Probability on Trees and Networks. Cambridge University Press (2012) (in preparation), Current version available at http://mypage.iu.edu/string~rdlyons/
  8. 8.
    MacCallum, I., Przybylski, D., Gnerre, S., Burton, J., Shlyakhter, I., Gnirke, A., Malek, J., McKernan, K., Ranade, S., Shea, T.P., Williams, L., Young, S., Nusbaum, C., Jaffe, D.B.: Allpaths 2: small genomes assembled accurately and with high continuity from short paired reads. Genome Biology 10(R103) (2009)Google Scholar
  9. 9.
    Miller, J.R., Koren, S., Sutton, G.: Assembly algorithms for next-generation sequencing data. Genomics 95, 315–327 (2010)CrossRefGoogle Scholar
  10. 10.
    Myers, E.W.: Toward simplifying and accurately formulating fragment assembly. Journal of Comutational Biology 2, 275–290 (1995)CrossRefGoogle Scholar
  11. 11.
    Myers, E.W., Sutton, G.G., Delcher, A.L., Dew, I.M., Fasulo, D.P., Flanigan, M.J., Kravitz, S.A., Mobarry, C.M., Reinert, K.H.J., Remington, K.A., Anson, E.L., Bolanos, R.A., Chou, H., Jordan, C.M., Halpern, A.L., Lonardi, S., Beasley, E.M., Brandon, R.C., Chen, L., Dunn, P.J., Lai, Z., Liang, Y., Nusskern, D.R., Zhan, M., Zhang, Q., Zheng, X., Rubin, G.M., Adams, M.D., Venter, J.C.: A whole-genome assembly of drosophila. Science 287, 2196–2204 (2000)CrossRefGoogle Scholar
  12. 12.
    Nurk, S., et al.: Assembling genomes and mini-metagenomes from highly chimeric reads. In: Deng, M., Jiang, R., Sun, F., Zhang, X. (eds.) RECOMB 2013. LNCS, vol. 7821, pp. 158–170. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Pevzner, P.A., Tang, H., Waterman, M.S.: An eulerian path approach to dna fragment assembly. Proceedings of the National Academy of Sciences 98, 9748–9753 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Pop, M.: Genome assembly reborn: recent computational challenges. Briefings in Bioinformatics 10(4), 354–366 (2009)CrossRefGoogle Scholar
  15. 15.
    Sahli, M., Shibuya, T.: Arapan-s: a fast and highly accurate whole-genome assembly software for viruses and small genomes. BMC Research Notes 5(243) (2012)Google Scholar
  16. 16.
    Simpson, J.T., Wong, K., Jackman, S.D., Schein, J.E., Jones, S.J.: Abyss: a parallel assembler for short read sequence data. Genome Research 19, 1117–1123 (2009)CrossRefGoogle Scholar
  17. 17.
    Zerbino, D.R., Birney, E.: Velvet: algorithms for de novo short read assembly using de bruijn graphs. Genome Research 18, 821–829 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Taku Onodera
    • 1
  • Kunihiko Sadakane
    • 2
  • Tetsuo Shibuya
    • 1
  1. 1.Human Genome Center, Institute of Medical ScienceUniversity of TokyoMinato-kuJapan
  2. 2.National Institute of InformaticsChiyoda-kuJapan

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