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ACOHAP: an efficient ant colony optimization for the haplotype inference by pure parsimony problem

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Abstract

Haplotype information plays an important role in many genetic analyses. However, the identification of haplotypes based on sequencing methods is both expensive and time consuming. Current sequencing methods are only efficient to determine conflated data of haplotypes, that is, genotypes. This raises the need to develop computational methods to infer haplotypes from genotypes.

Haplotype inference by pure parsimony is an NP-hard problem and still remains a challenging task in bioinformatics. In this paper, we propose an efficient ant colony optimization (ACO) heuristic method, named ACOHAP, to solve the problem. The main idea is based on the construction of a binary tree structure through which ants can travel and resolve conflated data of all haplotypes from site to site. Experiments with both small and large data sets show that ACOHAP outperforms other state-of-the-art heuristic methods. ACOHAP is as good as the currently best exact method, RPoly, on small data sets. However, it is much better than RPoly on large data sets. These results demonstrate the efficiency of the ACOHAP algorithm to solve the haplotype inference by pure parsimony problem for both small and large data sets.

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Notes

  1. http://sat.inesc-id.pt/~assg/rpoly/.

  2. http://www.iasi.cnr.it/~liuzzi/BIOCOMP/SNP/.

  3. http://doc.aporc.org/wiki/PTG.

  4. http://www.stats.ox.ac.uk/~marchini/phaseoff.html.

  5. Even though Table 3 of Benedettini et al. (2008) presents results of ACO-HI+ on the four SU data sets, only results concerning SU2 can be used for our comparisons. This is because of two reasons. First, results of ACO-HI+ for the SU1 data set are incorrectly reported in Benedettini et al. (2008); in fact, the sum of optimal solution values for the SU1 data set is 11961, which is much higher than 2453 as reported in Table 3 of Benedettini et al. (2008). Second, Table 3 of Benedettini et al. (2008) does not report the sum of the solution values of ACO-HI+ for all problem instances of the SU-100kb and SU3 data sets; this makes a comparison with our results impossible.

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Acknowledgements

We thank Gavin Band, Quang Le, and Khoi Le for comments and careful proof reading. We appreciate support from Graça and Tininini for providing us their programs. We also thank anonymous referees and editors for helpful comments and corrections. This work is partly supported by Vietnam National Science and Technology Fund (Nafosted: 102.01-2011.21).

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Authors and Affiliations

  1. Information Technology Institute, VNU, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam

    Dong Duc Do

  2. University of Engineering and Technology & Information Technology Institute, VNU, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam

    Sy Vinh Le

  3. University of Engineering and Technology, VNU, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam

    Xuan Huan Hoang

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  1. Dong Duc Do
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  2. Sy Vinh Le
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  3. Xuan Huan Hoang
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Correspondence to Sy Vinh Le.

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Do, D.D., Le, S.V. & Hoang, X.H. ACOHAP: an efficient ant colony optimization for the haplotype inference by pure parsimony problem. Swarm Intell 7, 63–77 (2013). https://doi.org/10.1007/s11721-013-0077-8

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