• Journal of KIISE:Computer Systems and Theory
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• v.35 no.11
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• pp.509-516
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• 2008

The Minimum Letter Flips (MLF) model and the Weighted Minimum Letter Flips (WMLF) model are for solving the haplotype assembly problem. But these two models are effective only when the error rate in SNP fragments is low. In this paper, we first establish a new computational model that employs the related genotype information as an improvement of the WMLF model and show its NP-hardness, and then propose an efficient genetic algorithm to solve the haplotype assembly problem. The results of experiments on random data set and a real data set indicate that the introduction of genotype information to the WMLF model is quite effective in improving the reconstruction rate especially when the error rate in SNP fragments is high. And the results also show that genotype information increases the convergence speed of the genetic algorithm.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.157-161
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• 2009

Minimum Letter Flips(MLF) and Weighted Minimum Letter Flips(WMLF) can perform the haplotype reconstruction more accurately from SNP fragments when they have many errors and gaps by introducing the related genotype information. And it is known that WMLF is more accurate in haplotype reconstruction than those based on the MLF. In the paper, we analyze two models under the conditions that the different rates of homozygous site in the genotype information and the different confidence levels according to the sequencing quality. We compare the performance of the two models using neural network and genetic algorithm. If the rate of homozygous site is high and sequencing quality is good, the results of experiments indicate that WMLF/GI has higher accuracy of haplotype reconstruction than that of the MCIH especially when the error rate and gap rate of SNP fragments are high.