Data Mining and Knowledge Discovery

, Volume 33, Issue 1, pp 1–23 | Cite as

Estimating sequence similarity from read sets for clustering next-generation sequencing data

  • Petr RyšavýEmail author
  • Filip Železný


Computing mutual similarity of biological sequences such as DNA molecules is essential for significant biological tasks such as hierarchical clustering of genomes. Current sequencing technologies do not provide the content of entire biological sequences; rather they identify a large number of small substrings called reads, sampled at random places of the target sequence. To estimate similarity of two sequences from their read-set representations, one may try to reconstruct each one first from its read set, and then employ conventional (dis)similarity measures such as the edit distance on the assembled sequences. Due to the nature of data, sequence assembly often cannot provide a single putative sequence that matches the true DNA. Therefore, 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, avoiding the sequence assembly step. For low-coverage (i.e. small) read set samples, it yields a better approximation of the true sequence similarities. This in turn results in better clustering in comparison to the first-assemble-then-cluster approach. Put differently, for a fixed estimation accuracy, our approach requires smaller read sets and thus entails reduced wet-lab costs.


Read sets Similarity Hierarchical clustering Biological sequences 



The authors acknowledge the support of the OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.

Supplementary material

10618_2018_584_MOESM1_ESM.pdf (267 kb)
Supplementary material 1 (pdf 267 KB)


  1. 1000 Genomes Project Consortium et al. (2015) A global reference for human genetic variation. Nature 526(7571):68–74Google Scholar
  2. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410CrossRefGoogle Scholar
  3. Bao E, Jiang T, Kaloshian I, Girke T (2011) SEED: efficient clustering of next-generation sequences. Bioinformatics 27(18):2502–2509CrossRefGoogle Scholar
  4. Blaisdell BE (1986) A measure of the similarity of sets of sequences not requiring sequence alignment. Proc Natl Acad Sci 83(14):5155–5159zbMATHCrossRefGoogle Scholar
  5. Comin M, Leoni A, Schimd M (2015) Clustering of reads with alignment-free measures and quality values. Algorithms Mol Biol 10(1):4CrossRefGoogle Scholar
  6. Comin M, Schimd M (2014) Assembly-free genome comparison based on next-generation sequencing reads and variable length patterns. BMC Bioinformatics 15(9):S1CrossRefGoogle Scholar
  7. Comin M, Schimd M (2016) Fast comparison of genomic and meta-genomic reads with alignment-free measures based on quality values. BMC Med Genomics 9(1):36CrossRefGoogle Scholar
  8. Fowlkes EB, Mallows CL (1983) A method for comparing two hierarchical clusterings. J Am Stat Assoc 78(383):553–569zbMATHCrossRefGoogle Scholar
  9. Goodwin S, Mcpherson J, Richard Mccombie W (2016) Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17:333–351 05CrossRefGoogle Scholar
  10. Haiminen N, Kuhn DN, Parida L, Rigoutsos I (2011) Evaluation of methods for de novo genome assembly from high-throughput sequencing reads reveals dependencies that affect the quality of the results. PLOS ONE 6(9):1–9 09CrossRefGoogle Scholar
  11. Hernandez D, Franois P, Farinelli L, sters M, Schrenzel J (2008) De novo bacterial genome sequencing: millions of very short reads assembled on a desktop computer. Genome Res 18(5):802–809CrossRefGoogle Scholar
  12. Huang W, Li L, Myers JR, Marth GT (2012) ART: a next-generation sequencing read simulator. Bioinformatics 28(4):593–594CrossRefGoogle Scholar
  13. Hubbard T, Barker D, Birney E, Cameron G, Chen Y et al (2002) The Ensembl genome database project. Nucl Acids Res 30(1):38–41CrossRefGoogle Scholar
  14. Jalovec K, Železný F (2014) Binary classification of metagenomic samples using discriminative DNA superstrings. In: MLSB 2014: 8th International workshop on machine learning in systems biology, pp 44–47Google Scholar
  15. Kchouk M, Elloumi M(2016) A clustering approach for denovo assembly using next generation sequencing data. In: 2016 IEEE international conference on bioinformatics and biomedicine (BIBM), IEEE, pp 1909–1911Google Scholar
  16. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W et al (2001) Initial sequencing and analysis of the human genome. Nature 409(6822):860–921CrossRefGoogle Scholar
  17. Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Trraga A, Cheng Y, Cleland I, Faruque N, Goodgame N, Gibson R, Hoad G, Jang M, Pakseresht N, Plaister S, Radhakrishnan R, Reddy K, Sobhany S, Ten Hoopen P, Vaughan R, Zalunin V, Cochrane G (2011) The European Nucleotide Archive. Nucl Acids Res 39(suppl–1):D28–D31CrossRefGoogle Scholar
  18. Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Sov Phys Dokl 10(8):707MathSciNetGoogle Scholar
  19. Malhotra R, Elleder D, Bao L, Hunter DR, Acharya R, Poss M (2014) Clustering pipeline for determining consensus sequences in targeted next-generation sequencing. ArXiv preprintGoogle Scholar
  20. Monge AE, Elkan CP (1996) The field matching problem: algorithms and applications. In: Proceedings of the second international conference on knowledge discovery and data mining, KDD’96, AAAI Press, pp 267–270Google Scholar
  21. Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33(1):31–88CrossRefGoogle Scholar
  22. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48(3):443–453CrossRefGoogle Scholar
  23. Nurk Sergey, Bankevich Anton, et al (2013) Assembling genomes and mini-metagenomes from highly chimeric reads. In: Deng M, Jiang R, Sun F, Zhang X, (eds) 17th Annual international conference on research in computational molecular biology, RECOMB 2013, Beijing, China, April 7–10, 2013. Proceedings, Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 158–170Google Scholar
  24. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, Phillippy AM (2016) Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 17(1):132CrossRefGoogle Scholar
  25. Reinert G, Chew D, Sun F, Waterman MS (2009) Alignment-free sequence comparison (I): statistics and power. J Comput Biol 16(12):1615–1634MathSciNetCrossRefGoogle Scholar
  26. Ryšavý Petr, Železný Filip (2016) Estimating sequence similarity from read sets for clustering sequencing data. In: Boström H, Knobbe A, Soares C, Papapetrou P (eds) 15th International symposium on advances in intelligent data analysis XV, IDA 2016, Stockholm, Sweden, October 13–15, 2016, Proceedings, Cham, Springer International Publishing, pp 204–214Google Scholar
  27. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4(4):406–425Google Scholar
  28. Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJM, nan Birol (2009) ABySS: a parallel assembler for short read sequence data. Genome Res 19(6):1117–1123CrossRefGoogle Scholar
  29. Sokal RR, Michener CD (1958) A statistical method for evaluating systematic relationships. Univ Kans Sci Bull 38:1409–1438Google Scholar
  30. Song K, Ren J, Zhai Z, Liu X, Deng M, Sun F (2013) Alignment-free sequence comparison based on next-generation sequencing reads. J Comput Biol 20(2):64–79MathSciNetCrossRefGoogle Scholar
  31. Ukkonen E (1992) Approximate string-matching with \(q\)-grams and maximal matches. Theor Comput Sci 92(1):191–211MathSciNetzbMATHCrossRefGoogle Scholar
  32. Wagner RA, Fischer MJ (1974) The string-to-string correction problem. J Assoc Comput Mach 21(1):168–173MathSciNetzbMATHCrossRefGoogle Scholar
  33. Warren RL, Sutton GG, Jones SJM, Holt RA (2007) Assembling millions of short DNA sequences using SSAKE. Bioinformatics 23(4):500–501CrossRefGoogle Scholar
  34. Weitschek E, Santoni D, Fiscon G, De Cola MC, Bertolazzi P, Felici G (2014) Next generation sequencing reads comparison with an alignment-free distance. BMC Res Notes 7:869CrossRefGoogle Scholar
  35. Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, Church DM, DiCuccio M, Edgar R, Federhen S, Feolo M, Geer LY, Helmberg W, Kapustin Y, Khovayko O, Landsman D, Lipman DJ, Madden TL, Maglott DR, Miller V, Ostell J, Pruitt KD, Schuler GD, Shumway M, Sequeira E, Sherry ST, Sirotkin K, Souvorov A, Starchenko G, Tatusov RL, Tatusova TA, Wagner L, Yaschenko E (2008) Database resources of the national center for biotechnology information. Nucl Acids Res 36(suppl–1):D13–D21Google Scholar
  36. Yi H, Jin L (2013) Co-phylog: an assembly-free phylogenomic approach for closely related organisms. Nucl Acids Res 41(7):e75CrossRefGoogle Scholar
  37. Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18(5):821–829CrossRefGoogle Scholar
  38. Železný F, Jalovec K, Tolar J (2014) Learning meets sequencing: a generality framework for read-sets. In: ILP 2014: 24th Internation conference on inductive logic programming, Late-Breaking PapersGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

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

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