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

Part of the Lecture Notes in Computer Science book series (LNBI,volume 10252)


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


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

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  1. Amir, A., Lewenstein, M., Lewenstein, N.: Pattern matching in hypertext. J. Algorithms 35(1), 82–99 (2000)

    CrossRef  MathSciNet  MATH  Google Scholar 

  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)

    CrossRef  MathSciNet  Google Scholar 

  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)

    CrossRef  MathSciNet  Google Scholar 

  4. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press, 2nd edn. (2001)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  9. Kim, D., Langmead, B., Salzberg, S.L.: HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12(4), 357–360 (2015)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. Navarro, G.: Improved approximate pattern matching on hypertext. Theoret. Comput. Sci. 237(1), 455–463 (2000)

    CrossRef  MathSciNet  MATH  Google Scholar 

  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

    CrossRef  Google Scholar 

  15. Rhoads, A., Au, K.F.: PacBio sequencing and its applications. Genomics Proteomics Bioinform. 13(5), 278–289 (2015). sI: Metagenomics of Marine Environments

    Google Scholar 

  16. Sirén, J.: Indexing variation graphs. CoRR abs/1604.06605 (2016)

    Google Scholar 

  17. Thachuk, C.: Indexing hypertext. J. Discrete Algorithms 18, 113–122 (2013)

    CrossRef  MathSciNet  MATH  Google Scholar 

  18. Trapnell, C., Pachter, L., Salzberg, S.L.: TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25(9), 1105–1111 (2009)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

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We thank the anonymous reviewers for their insightful comments.

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Correspondence to Luca Denti .

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Beretta, S., Bonizzoni, P., Denti, L., Previtali, M., Rizzi, R. (2017). Mapping RNA-seq Data to a Transcript Graph via Approximate Pattern Matching to a Hypertext. In: Figueiredo, D., Martín-Vide, C., Pratas, D., Vega-Rodríguez, M. (eds) Algorithms for Computational Biology. AlCoB 2017. Lecture Notes in Computer Science(), vol 10252. Springer, Cham.

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