Navigating in a Sea of Repeats in RNA-seq without Drowning

  • Gustavo Sacomoto
  • Blerina Sinaimeri
  • Camille Marchet
  • Vincent Miele
  • Marie-France Sagot
  • Vincent Lacroix
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8701)

Abstract

The main challenge in de novo assembly of NGS data is certainly to deal with repeats that are longer than the reads. This is particularly true for RNA-seq data, since coverage information cannot be used to flag repeated sequences, of which transposable elements are one of the main examples. Most transcriptome assemblers are based on de Bruijn graphs and have no clear and explicit model for repeats in RNA-seq data, relying instead on heuristics to deal with them. The results of this work are twofold. First, we introduce a formal model for representing high copy-number repeats in RNA-seq data and exploit its properties to infer a combinatorial characteristic of repeat-associated subgraphs. We show that the problem of identifying in a de Bruijn graph a subgraph with this characteristic is NP-complete. In a second step, we show that in the specific case of a local assembly of alternative splicing (AS) events, using our combinatorial characterization we can implicitly avoid such subgraphs. In particular, we designed and implemented an algorithm to efficiently identify AS events that are not included in repeated regions. Finally, we validate our results using synthetic data. We also give an indication of the usefulness of our method on real data.

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References

  1. 1.
    Bern, M., Plassmann, P.: The steiner problem with edge lengths 1 and 2. Information Processing Letters (1989)Google Scholar
  2. 2.
    Carroll, M.L., Roy-Engel, A.M., Nguyen, S.V., Salem, A.-H., et al.: Large-scale analysis of the alu ya5 and yb8 subfamilies and their contribution to human genomic diversity. Journal of Molecular Biology 311(1), 17–40 (2001)CrossRefGoogle Scholar
  3. 3.
    Djebali, S., Davis, C., Merkel, A., Dobin, A., et al.: Landscape of transcription in human cells. Nature (2012)Google Scholar
  4. 4.
    Grabherr, M., Haas, B., Yassour, M., Levin, J., et al.: Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biot. (2011)Google Scholar
  5. 5.
    Griebel, T., Zacher, B., Ribeca, P., Raineri, E., et al.: Modelling and simulating generic RNA-Seq experiments with the flux simulator. Nucleic Acids Res. (2012)Google Scholar
  6. 6.
    Jurka, J., Bao, W., Kojima, K.: Families of transposable elements, population structure and the origin of species. Biology Direct 6(1), 44 (2011)CrossRefGoogle Scholar
  7. 7.
    Kent, W.J.: BLAT–the BLAST-like alignment tool. Genome Res. 12 (2002)Google Scholar
  8. 8.
    Myers, E., Sutton, G., Delcher, A., Dew, I., et al.: A whole-genome assembly of drosophila. Science 287(5461), 2196–2204 (2000)CrossRefGoogle Scholar
  9. 9.
    Novák, P., Neumann, P., Macas, J.: Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data. BMC Bioinf. (2010)Google Scholar
  10. 10.
    Peng, Y., Leung, H., Yiu, S.-M., Lv, M.-J., et al.: IDBA-tran: a more robust de novo de bruijn graph assembler for transcriptomes with uneven expression levels. Bioinf. 29(13) (2013)Google Scholar
  11. 11.
    Robertson, G., Schein, J., Chiu, R., Corbett, R., et al.: De novo assembly and analysis of RNA-seq data. Nat. Met. 7(11), 909–912 (2010)CrossRefGoogle Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Sacomoto, G., Lacroix, V., Sagot, M.-F.: A polynomial delay algorithm for the enumeration of bubbles with length constraints in directed graphs and its application to the detection of alternative splicing in RNA-seq data. In: Darling, A., Stoye, J. (eds.) WABI 2013. LNCS, vol. 8126, pp. 99–111. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Schulz, M., Zerbino, D., Vingron, M., Birney, E.: Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinf. (2012)Google Scholar
  15. 15.
    Smit, A.F.A., Hubley, R., Green, P.: RepeatMasker Open-3.0, 1996-2004Google Scholar
  16. 16.
    Tilgner, H., Knowles, D., Johnson, R., Davis, C., et al.: Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs. Genome Res. (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Gustavo Sacomoto
    • 1
    • 2
  • Blerina Sinaimeri
    • 1
    • 2
  • Camille Marchet
    • 1
    • 2
  • Vincent Miele
    • 2
  • Marie-France Sagot
    • 1
    • 2
  • Vincent Lacroix
    • 1
    • 2
  1. 1.INRIA Grenoble Rhône-AlpesFrance
  2. 2.UMR CNRS 5558 - LBBEUniversité Lyon 1France

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