1. An Application of Genetic Algorithm to DNA Sequencing by Oligonucleotide Hybridization
Author ：Shigeomi HARA
Hiroshi DOUZONO
Yoshio NOGUCHI
Graduate：Chien-Ming Hsiao

2. Outline Motivation Objective Introduction
Oligonucleotide hybridization
Application of Genetic algorithm to the hybridization
GA methodology
Experiments
Conclusion
Opinion

3. Motivation
The target sequence reconstructed in the hybridization method is relatively long for GA, so special setups of the genetic operation will be necessary.

4. Objective
Introduce the grouping GA and special crossover method for target sequence reconstructed in the hybridization method .

5. Introduction
Hybridization method identifies the sequence by hybridization the target DNA sequence directly to the set of the probes which are short DNA sub-sequences of specific length, thus target DNA sequence can be identified in only an biochemical experiments.

6. Introduction
Grouping GA is introduce to keep the polymorphism of the individuals and special crossover method.
The reconstructed sequence may not be completely as same as the original sequence because of the ambiguity of the hybridization problem.

7. Oligonucleotide hybridization

8. Application of Genetic algorithm to the hybridization
Informational basis to apply GA
It is improvement of the searching method employing genetic algorithm.
A reconstructed sequences is assigned to an individual for GA in our algorithm.
The length of the chromosome will be changed as the progress of the reconstruction.
Special crossover method will be required.

9. Application of Genetic algorithm to the hybridization
Informational basis to apply GA
The number of the probes becomes for probe with N length.
The fitness function for this multiple object is

10. GA methodology
The initial population can be generated by the random search of the sequences that satisfy the connection rule.
Crossover
The constraint based crossover generates the individual which satisfies the connection rule.

11. GA methodology

12. GA methodology
Mutation
The mutations keep the connection rule.

13. Experimental results Number of groups 20
Number of individual for each group 20
Probability to generate new individual by crossover 0.6
Probability to mate with the individual in other group 0.01
Probability to generate new individual by mutation 0.3
Probability to generate new individual by random search 0.1
The parameter of fitness function : CS 80

14. Experiments for short (1000bp) sequences

15. Experiments

16. Experiments

17. Experiments

18. Experiments

19. Conclusion
This method will be available to the DNA sequence of bp or so on considering the preservation rates of sub-sequences.
It introduced the fitness function and crossover method which can handle miss hybridization and the results are improved to some degree, but not enough.

20. Opinion
We should develop the another algorithm which can reconstruct longer sequences(more than 1M bp) by using another techniques such as learning of the existing sequence.