Fast Mapping and Precise Alignment of AB SOLiD Color Reads to Reference DNA

  • Miklós Csűrös
  • Szilveszter Juhos
  • Attila Bérces
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6293)


Applied Biosystems’ SOLiD system offers a low-cost alternative to the traditional Sanger method of DNA sequencing. We introduce two main algorithms of mapping SOLiD’s color reads onto a reference genome. The first method performs mapping by adapting a greedy alignment framework. In such an alignment, reads are mapped to approximate genome positions, allowing for a pre-specified bound on sequence difference that combines nucleotide mismatches, gaps, and sequencing errors. The second method for precise alignment relies on a pair hidden Markov model framework, combining a DNA sequence evolution model and sequencing errors (from read quality files).


Sequencing Error Edit Distance Statistical Alignment Color Sequence Precise Alignment 
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  1. 1.
    Shendure, J., Li, H.: Next-generation DNA sequencing. Nat. Biotechnol. 26(10), 1135–1145 (2008)CrossRefPubMedGoogle Scholar
  2. 2.
    Shendure, J., Mitra, R.D., Varma, C., Church, G.M.: Advanced sequencing technologies: Methods and goals. Nat. Rev. Genet. 5, 335–344 (2004)CrossRefPubMedGoogle Scholar
  3. 3.
    Wheeler, D.A., et al.: The complete genome of an individual by massively parallel DNA sequencing. Nature 452, 872–876 (2008)CrossRefPubMedGoogle Scholar
  4. 4.
    Pleasance, E.D., et al.: A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2010)CrossRefPubMedGoogle Scholar
  5. 5.
    Venter, J.C., et al.: Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66–74 (2004)CrossRefPubMedGoogle Scholar
  6. 6.
    Flicek, P., Birney, E.: Sense from sequence reads: methods for alignment and assembly. Nat. Methods 6(11s), S6–S12 (2009)Google Scholar
  7. 7.
    Li, H., Durbin, R.: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14), 1754–1760 (2009)CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L.: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10 (2009)Google Scholar
  9. 9.
    Brown, D.G., Li, M., Ma, B.: A tutorial of recent developments in the seeding of local alignment. J. Bioinform. Comput. Biol. 2(4), 819–842 (2004)CrossRefPubMedGoogle Scholar
  10. 10.
    Medvedev, P., Stanciu, M., Brudno, M.: Computational methods for discovering structural variation with next-generation sequencing. Nat. Methods 6(11s), S13–S20 (2009)Google Scholar
  11. 11.
    Huson, D.H., Auch, A.F., Qi, J., Schuster, S.C.: MEGAN analysis of metagenomic data. Genome Res. 17, 377–386 (2007)Google Scholar
  12. 12.
    Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)CrossRefPubMedGoogle Scholar
  13. 13.
    Gotoh, O.: An improved algorithm for matching biological sequences. J. Mol. Biol. 162(3), 705–708 (1982)CrossRefPubMedGoogle Scholar
  14. 14.
    Rumble, S.M., Lacroute, P., Dalca, A.V., Fiume, M., Sidow, A., Brudno, M.: SHRiMP: Accurate mapping of short color-space reads. PLoS Comput. Biol. 5(5), e1000386 (2009)Google Scholar
  15. 15.
    Homer, N., Merriman, B., Nelson, S.F.: Local alignment of two-base encoded DNA sequence. BMC Bioinformatics 10, 175 (2009)CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Wu, S., Manber, U., Myers, G., Miller, W.: An O(NP) sequence comparison algorithm. Inform. Process. Lett. 35(6), 317–323 (1990)CrossRefGoogle Scholar
  17. 17.
    Zhang, Z., Schwartz, S., Wagner, L., Miller, W.: A greedy alignment for aligning DNA sequences. J. Comput. Biol. 7(1/2), 203–214 (2000)CrossRefPubMedGoogle Scholar
  18. 18.
    Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, UK (1998)CrossRefGoogle Scholar
  19. 19.
    Lunter, G., Drummond, A.J., Miklós, I., Hein, J.: Statistical alignment: Recent progress, new applications, and challenges. In: Nielsen, R. (ed.) Statistical Methods in Molecular Evolution. Springer, Heidelberg (2005)Google Scholar
  20. 20.
    Rabiner, L.R.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  21. 21.
    Ewing, B., Green, P.: Base-calling of automated sequencer traces using phred: II. error probabilities. Genome Res. 8, 186–194 (1998)CrossRefPubMedGoogle Scholar
  22. 22.
    Liò, P., Goldman, N.: Models of molecular evolution and phylogeny. Genome Res. 8, 1233–1244 (1998)CrossRefPubMedGoogle Scholar
  23. 23.
    Felsenstein, J., Churchill, G.A.: A Hidden Markov Model approach to variation among sites in rate of evolution. Mol. Biol. Evol. 13(1), 93–104 (1996)CrossRefPubMedGoogle Scholar
  24. 24.
    Schwartz, S.A., Pachter, L.: Multiple alignment by sequence annealing. Bioinformatics 23(2), 24–29 (2007)CrossRefGoogle Scholar
  25. 25.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410 (1990)CrossRefPubMedGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Miklós Csűrös
    • 1
  • Szilveszter Juhos
    • 2
  • Attila Bérces
    • 2
  1. 1.Department of Computer Science and Operations ResearchUniversity of MontréalCanada
  2. 2.Omixon, Chemistry Logic KftBudapestHungary

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