Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data

  • Petr RyšavýEmail author
  • Filip Železný
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)


Clustering biological sequences is a central task in bioinformatics. The typical result of new-generation sequencers is a set of short substrings (“reads”) of a target sequence, rather than the sequence itself. To cluster sequences given only their read-set representations, one may try to reconstruct each one from the corresponding read set, and then employ conventional (dis)similarity measures such as the edit distance on the assembled sequences. This approach is however problematic and we propose instead to estimate the similarities directly from the read sets. Our approach is based on an adaptation of the Monge-Elkan similarity known from the field of databases. It avoids the NP-hard problem of sequence assembly and in empirical experiments it results in a better approximation of the true sequence similarities and consequently in better clustering, in comparison to the first-assemble-then-cluster approach.


Read sets Similarity Hierarchical clustering 



This work was supported by Czech Science Foundation project 14-21421S.


  1. 1.
    Bao, E., Jiang, T., Kaloshian, I., Girke, T.: Seed: efficient clustering of next-generation sequences. Bioinformatics 27(18), 2502–2509 (2011)Google Scholar
  2. 2.
    Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553–569 (1983)CrossRefzbMATHGoogle Scholar
  3. 3.
    Hernandez, D., et al.: De novo bacterial genome sequencing: millions of very short reads assembled on a desktop computer. Genome Res. 18(5), 802–809 (2008)CrossRefGoogle Scholar
  4. 4.
    Jalovec, K., Železný, F.: Binary classification of metagenomic samples using discriminative dna superstrings. In: 8th International Workshop on Machine Learning in Systems Biology, MLSB 2014 (2014)Google Scholar
  5. 5.
    Lander, E.: Initial impact of the sequencing of the human genome. Nature 470(7333), 187–197 (2011)CrossRefGoogle Scholar
  6. 6.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Malhotra, R., Elleder, D., Bao, L., Hunter, D.R., Acharya, R., Poss, M.: Clustering pipeline for determining consensus sequences in targeted next-generation sequencing. arXiv (Conrell University Library) arXiv:1410.1608 (2016)
  8. 8.
    Monge, A.E., Elkan, C.P.: The webfind tool for finding scientific papers over the worldwide web. In: Proceedings of the 3rd International Congress on Computer Science Research (1996)Google Scholar
  9. 9.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)CrossRefGoogle Scholar
  10. 10.
    Saitou, N., Nei, M.: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4(4), 406–425 (1987)Google Scholar
  11. 11.
    Simpson, J.T., et al.: ABySS: a parallel assembler for short read sequence data. Genome Res. 9(6), 1117–1123 (2009)CrossRefGoogle Scholar
  12. 12.
    Sokal, R.R., Michener, C.D.: A statistical method for evaluating systematic relationships. Univ. Kansas Sci. Bull. 38, 1409–1438 (1958)Google Scholar
  13. 13.
    Železný, F., Jalovec, K., Tolar, J.: Learning meets sequencing: a generality framework for read-sets. In: 24th International Conference on Inductive Logic Programming, Late-Breaking Papers, ILP 2014 (2014)Google Scholar
  14. 14.
    Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. J. ACM 21(1), 168–173 (1974). MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Warren, R.L., et al.: Assembling millions of short DNA sequences using SSAKE. Bioinformatics 23(4), 500–501 (2007)CrossRefGoogle Scholar
  16. 16.
    Weitschek, E., Santoni, D., Fiscon, G., Cola, M.C.D., Bertolazzi, P., Felici, G.: Next generation sequencing reads comparison with an alignment-free distance. BMC Res. Notes 7(1), 869 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic

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