Xiang-Sun Zhang¹, Rui-Sheng Wang², Ling-Yun Wu¹ and Wei Zhang³

¹Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China. ²School of Information, Renmin University of China, Beijing 100872. ³North Carolina State University, Raleigh, NC 27695-7906, U.S.A.

Abstract: The Minimum Error Correction (MEC) is an important model for haplotype reconstruction from SNP fragments. However, this model is effective only when the error rate of SNP fragments is low. In this paper, we propose a new computational model called Minimum Conflict Individual Haplotyping (MCIH) as an extension to MEC. In contrast to the conventional approaches, the new model employs SNP fragment information and also related genotype information, thereby a high accurate inference can be expected. We first prove the MCIH problem to be NP-hard. To evaluate the practicality of the new model we design an exact algorithm (a dynamic programming procedure) to implement MCIH on a special data structure. The numerical experience indicates that it is fairly effective to use MCIH at the cost of related genotype information, especially in the case of SNP fragments with a high error rate. Moreover, we present a feed-forward neural network algorithm to solve MCIH for general data structure and large size instances. Numerical results on real biological data and simulation data show that the algorithm works well and MCIH is a potential alternative in individual haplotyping.