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Mapping RNA-seq Data to a Transcript Graph via Approximate Pattern Matching to a Hypertext

  • Stefano Beretta
  • Paola Bonizzoni
  • Luca Denti
  • Marco Previtali
  • Raffaella Rizzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10252)

Abstract

Graphs are the most suited data structure to summarize the transcript isoforms produced by a gene. Such graphs may be modeled by the notion of hypertext, that is a graph where nodes are texts representing the exons of the gene and edges connect consecutive exons of a transcript. Mapping reads obtained by deep transcriptome sequencing to such graphs is crucial to compare reads with an annotation of transcript isoforms and to infer novel events due to alternative splicing at the exonic level.

In this paper, we propose an algorithm based on Maximal Exact Matches that efficiently solves the approximate pattern matching of a pattern P to a hypertext H. We implement it into Splicing Graph ALigner (SGAL), a tool that performs an accurate mapping of RNA-seq reads against a graph that is a representation of annotated and potentially new transcripts of a gene. Moreover, we performed an experimental analysis to compare SGAL to a state-of-art tool for spliced alignment (STAR), and to identify novel putative alternative splicing events such as exon skipping directly from mapping reads to the graph. Such analysis shows that our tool is able to perform accurate mapping of reads to exons, with good time and space performance.

The software is freely available at https://github.com/AlgoLab/galig.

Keywords

Approximate sequence analysis Next-generation sequencing Alternative splicing Graph-based alignment 

Notes

Acknowledgments

We thank the anonymous reviewers for their insightful comments.

References

  1. 1.
    Amir, A., Lewenstein, M., Lewenstein, N.: Pattern matching in hypertext. J. Algorithms 35(1), 82–99 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Beretta, S., Bonizzoni, P., Della Vedova, G., Pirola, Y., Rizzi, R.: Modeling alternative splicing variants from RNA-seq data with isoform graphs. J. Comput. Biol. 21(1), 16–40 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bonizzoni, P., Della Vedova, G., Pirola, Y., Previtali, M., Rizzi, R.: LSG: an external-memory tool to compute string graphs for next-generation sequencing data assembly. J. Comput. Biol. 23(3), 137–149 (2016)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press, 2nd edn. (2001)Google Scholar
  5. 5.
    Dilthey, A., Cox, C., Iqbal, Z., Nelson, M.R., McVean, G.: Improved genome inference in the MHC using a population reference graph. Nat. Genet. 47(6), 682–688 (2015)CrossRefGoogle Scholar
  6. 6.
    Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., Gingeras, T.R.: STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1), 15–21 (2013)CrossRefGoogle Scholar
  7. 7.
    Heber, S., Alekseyev, M., Sze, S.H., Tang, H., Pevzner, P.A.: Splicing graphs and EST assembly problem. Bioinformatics 18(suppl. 1), S181–S188 (2002)CrossRefGoogle Scholar
  8. 8.
    Horner, D.S., Pavesi, G., Castrignanò, T., De Meo, P.D., Liuni, S., Sammeth, M., Picardi, E., Pesole, G.: Bioinformatics approaches for genomics and post genomics applications of next-generation sequencing. Briefings Bioinf. 11(2), 181–197 (2010)CrossRefGoogle Scholar
  9. 9.
    Kim, D., Langmead, B., Salzberg, S.L.: HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12(4), 357–360 (2015)CrossRefGoogle Scholar
  10. 10.
    Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., Salzberg, S.L.: TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14(4), R36 (2013)CrossRefGoogle Scholar
  11. 11.
    Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G.T., Abecasis, G.R., Durbin, R.: The sequence alignment/map format and SAMtools. Bioinformatics 25(16), 2078–2079 (2009)CrossRefGoogle Scholar
  12. 12.
    Manber, U., Wu, S.: Approximate string matching with arbitrary costs for text and hypertext. In: Proceedings of the IAPR International Workshop on Structural and Syntactic Pattern Recognition, pp. 22–33 (1993)Google Scholar
  13. 13.
    Navarro, G.: Improved approximate pattern matching on hypertext. Theoret. Comput. Sci. 237(1), 455–463 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Ohlebusch, E., Gog, S., Kügel, A.: Computing matching statistics and maximal exact matches on compressed full-text indexes. In: Chavez, E., Lonardi, S. (eds.) SPIRE 2010. LNCS, vol. 6393, pp. 347–358. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-16321-0_36 CrossRefGoogle Scholar
  15. 15.
    Rhoads, A., Au, K.F.: PacBio sequencing and its applications. Genomics Proteomics Bioinform. 13(5), 278–289 (2015). sI: Metagenomics of Marine EnvironmentsGoogle Scholar
  16. 16.
    Sirén, J.: Indexing variation graphs. CoRR abs/1604.06605 (2016)Google Scholar
  17. 17.
    Thachuk, C.: Indexing hypertext. J. Discrete Algorithms 18, 113–122 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Trapnell, C., Pachter, L., Salzberg, S.L.: TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25(9), 1105–1111 (2009)CrossRefGoogle Scholar
  19. 19.
    Vyverman, M., De Baets, B., Fack, V., Dawyndt, P.: essaMEM: finding maximal exact matches using enhanced sparse suffix arrays. Bioinformatics 29(6), 802–804 (2013)CrossRefGoogle Scholar
  20. 20.
    Yeoh, L.M., Goodman, C.D., Hall, N.E., van Dooren, G.G., McFadden, G.I., Ralph, S.A.: A serine-arginine-rich (SR) splicing factor modulates alternative splicing of over a thousand genes in Toxoplasma gondii. Nucleic Acids Res. 43(9), 4661–4675 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Stefano Beretta
    • 1
  • Paola Bonizzoni
    • 1
  • Luca Denti
    • 1
  • Marco Previtali
    • 1
  • Raffaella Rizzi
    • 1
  1. 1.Department of Informatics, Systems and Communication (DISCo)University of Milan–BicoccaMilanItaly

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