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Describing the Local Structure of Sequence Graphs

  • Yohei Rosen
  • Jordan Eizenga
  • Benedict Paten
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10252)

Abstract

Analysis of genetic variation using graph structures is an emerging paradigm of genomics. However, defining genetic sites on sequence graphs remains an open problem. Paten’s invention of the ultrabubble and snarl, special subgraphs of sequence graphs which can identified with efficient algorithms, represents important first step to segregating graphs into genetic sites. We extend the theory of ultrabubbles to a special subclass where every detail of the ultrabubble can be described in a series and parallel arrangement of genetic sites. We furthermore introduce the concept of bundle structures, which allows us to recognize the graph motifs created by additional combinations of variation in the graph, including but not limited to runs of abutting single nucleotide variants. We demonstrate linear-time identification of bundles in a bidirected graph. These two advances build on initial work on ultrabubbles in bidirected graphs, and define a more granular concept of genetic site.

Keywords

Sequence graphs Genetic variants 

Notes

Acknowledgements

Y.R. is supported by a Howard Hughes Medical Institute Medical Research Fellowship. This work was also supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number 5U54HG007990 and grants from the W.M. Keck foundation and the Simons Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank Wolfgang Beyer for his visualizations of 1000 Genomes data in a variation graph.

References

  1. 1.
    1000 Genomes Project Consortium, et al.: A global reference for human genetic variation. Nature 526(7571), 68–74 (2015)Google Scholar
  2. 2.
    Beyer, W.: Sequence tube maps (2016). https://github.com/wolfib/sequenceTubeMap
  3. 3.
    Brankovic, L., Iliopoulos, C.S., Kundu, R., Mohamed, M., Pissis, S.P., Vayani, F.: Linear-time superbubble identification algorithm for genome assembly. Theor. Comput. Sci. 609(Pt. 2), 374–383 (2016). http://www.sciencedirect.com/science/article/pii/S0304397515009147
  4. 4.
    Danecek, P., Auton, A., Abecasis, G., Albers, C.A., Banks, E., DePristo, M.A., Handsaker, R.E., Lunter, G., Marth, G.T., Sherry, S.T., et al.: The variant call format and vcftools. Bioinformatics 27(15), 2156–2158 (2011)CrossRefGoogle Scholar
  5. 5.
    Duffin, R.: Topology of series-parallel networks. J. Math. Anal. Appl. 10(2), 303–318 (1965). http://www.sciencedirect.com/science/article/pii/0022247X65901253
  6. 6.
    Medvedev, P., Brudno, M.: Maximum likelihood genome assembly. J. Comput. Biol. 16(8), 1101–1116 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Novak, A.M., Hickey, G., Garrison, E., Blum, S., Connelly, A., Dilthey, A., Eizenga, J., Elmohamed, M.A.S., Guthrie, S., Kahles, A., Keenan, S., Kelleher, J., Kural, D., Li, H., Lin, M.F., Miga, K., Ouyang, N., Rakocevic, G., Smuga-Otto, M., Zaranek, A.W., Durbin, R., McVean, G., Haussler, D., Paten, B.: Genome graphs. bioRxiv (2017). http://biorxiv.org/content/early/2017/01/18/101378
  8. 8.
    Onodera, T., Sadakane, K., Shibuya, T.: Detecting superbubbles in assembly graphs. In: Darling, A., Stoye, J. (eds.) WABI 2013. LNCS, vol. 8126, pp. 338–348. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40453-5_26 CrossRefGoogle Scholar
  9. 9.
    Paten, B., Novak, A.M., Garrison, E., Hickey, G.: Superbubbles, ultrabubbles and cacti. bioRxiv (2017). http://biorxiv.org/content/early/2017/01/18/101493
  10. 10.
    Sudmant, P.H., Rausch, T., Gardner, E.J., Handsaker, R.E., Abyzov, A., Huddleston, J., Zhang, Y., Ye, K., Jun, G., Fritz, M.H.Y., et al.: An integrated map of structural variation in 2,504 human genomes. Nature 526(7571), 75–81 (2015)CrossRefGoogle Scholar
  11. 11.
    Sung, W.K., Sadakane, K., Shibuya, T., Belorkar, A., Pyrogova, I.: An o(m log m)-time algorithm for detecting superbubbles. IEEE/ACM Trans. Comput. Biol. Bioinform. 12(4), 770–777. https://doi.org/10.1109/TCBB.2014.2385696
  12. 12.
    Valdes, J., Tarjan, R.E., Lawler, E.L.: The recognition of series parallel digraphs. SIAM J. Comput. 11(2), 298–313 (1982). http://dx.doi.org/10.1137/0211023
  13. 13.
    Zerbino, D.R., Birney, E.: Velvet: algorithms for de novo short read assembly using de bruijn graphs. Genome Res. 18(5), 821–829 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.New York University School of MedicineNew YorkUSA
  2. 2.University of California Santa Cruz Genomics InstituteSanta CruzUSA

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