DNA Fragment Assembly by Ant Colony and Nearest Neighbour Heuristics

  • Wannasak Wetcharaporn
  • Nachol Chaiyaratana
  • Sissades Tongsima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


This paper presents the use of a combined ant colony system (ACS) and nearest neighbour heuristic (NNH) algorithm in DNA fragment assembly. The assembly process can be treated as combinatorial optimisation where the aim is to find the right order of each fragment in the ordering sequence that leads to the formation of a consensus sequence that truly reflects the original DNA strands. The assembly procedure proposed is composed of two stages: fragment assembly and contiguous sequence (contig) assembly. In the fragment assembly stage, a possible alignment between fragments is determined where the fragment ordering sequence is created using the ACS algorithm. The resulting contigs are then assembled together using the NNH rule. The results indicate that in overall the performance of the combined ACS/NNH technique is superior to that of a standard sequence assembly program (CAP3), which is widely used by many genomic institutions.


Travel Salesman Problem Travel Salesman Problem Reverse Complement CAP3 Program Fragment Assembly 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Applewhite, A.: Mining the genome. IEEE Spectrum 39(4), 69–71 (2002)CrossRefGoogle Scholar
  2. 2.
    Pop, M., Salzberg, S.L., Shumway, M.: Genome sequence assembly: Algorithms and issues. Computer 35(7), 47–54 (2002)CrossRefGoogle Scholar
  3. 3.
    Huang, X., Madan, A.: CAP3: A DNA sequence assembly program. Genome Research 9(9), 868–877 (1999)CrossRefGoogle Scholar
  4. 4.
    Green, P.: Phrap documentation. Phred, Phrap, and Consed (2004),
  5. 5.
    Ferreira, C.E., de Souza, C.C., Wakabayashi, Y.: Rearrangement of DNA fragments: A branch-and-cut algorithm. Discrete Applied Mathematics 116(1-2), 161–177 (2002)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Batzoglou, S., Jaffe, D., Stanley, K., Butler, J., Gnerre, S., Mauceli, E., Berger, B., Mesirov, J.P., Lander, E.S.: ARACHNE: A whole-genome shotgun assembler. Genome Research 12(1), 177–189 (2002)CrossRefGoogle Scholar
  7. 7.
    Kececioglu, J.D., Myers, E.W.: Combinatorial algorithms for DNA sequence assembly. Algorithmica 13(1-2), 7–51 (1995)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Pevzner, P.A., Tang, H., Waterman, M.S.: An Eulerian path approach to DNA fragment assembly. Proceedings of the National Academy of Sciences of the United States of America 98(17), 9748–9753 (2001)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Burks, C., Engle, M., Forrest, S., Parsons, R., Soderlund, C., Stolorz, P.: Stochastic optimization tools for genomic sequence assembly. In: Adams, M.D., Fields, C., Venter, J.C. (eds.) Automated DNA Sequencing and Analysis, pp. 249–259. Academic Press, London (1994)Google Scholar
  10. 10.
    Parsons, R.J., Forrest, S., Burks, C.: Genetic algorithms, operators, and DNA fragment assembly. Machine Learning 21(1-2), 11–33 (1995)CrossRefGoogle Scholar
  11. 11.
    Parsons, R.J., Johnson, M.E.: A case study in experimental design applied to genetic algorithms with applications to DNA sequence assembly. American Journal of Mathematical and Management Sciences 17(3-4), 369–396 (1997)Google Scholar
  12. 12.
    Kim, K., Mohan, C.K.: Parallel hierarchical adaptive genetic algorithm for fragment assembly. In: Proceedings of the 2003 Congress on Evolutionary Computation, Canberra, Australia, pp. 600–607 (2003)Google Scholar
  13. 13.
    Angeleri, E., Apolloni, B., de Falco, D., Grandi, L.: DNA fragment assembly using neural prediction techniques. International Journal of Neural Systems 9(6), 523–544 (1999)CrossRefGoogle Scholar
  14. 14.
    Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  15. 15.
    Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. Journal of Molecular Biology 147(1), 195–197 (1981)CrossRefGoogle Scholar
  16. 16.
    Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Wheeler, D.L.: GenBank. Nucleic Acids Research 33, D34–D38 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wannasak Wetcharaporn
    • 1
  • Nachol Chaiyaratana
    • 1
    • 2
  • Sissades Tongsima
    • 3
  1. 1.Research and Development Center for Intelligent SystemsKing Mongkut’s Institute of Technology North BangkokBangsue, BangkokThailand
  2. 2.Institute of Field RoboticsKing Mongkut’s University of Technology ThonburiBangkokThailand
  3. 3.National Center for Genetic Engineering and Biotechnology, National Science and Technology Development AgencyPathumthaniThailand

Personalised recommendations