Haplotyping as perfect phylogeny: conceptual framework and efficient solutions
Proceedings of the sixth annual international conference on Computational biology
The Haplotyping problem: an overview of computational models and solutions
Journal of Computer Science and Technology
Islands of Tractability for Parsimony Haplotyping
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
2SNP: scalable phasing based on 2-SNP haplotypes
Bioinformatics
Haplotyping Populations by Pure Parsimony: Complexity of Exact and Approximation Algorithms
INFORMS Journal on Computing
CollHaps: A Heuristic Approach to Haplotype Inference by Parsimony
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SplittingHeirs: inferring haplotypes by optimizing resultant dense graphs
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
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An enormous amount of sequence data has been generated with the development of new DNA sequencing technologies, which presents great challenges for computational biology problems such as haplotype phasing. Although arduous efforts have been made to address this problem, the current methods still cannot efficiently deal with the incoming flood of large-scale data. In this paper, we propose a flow network model to tackle haplotype phasing problem, and explain some classical haplotype phasing rules based on this model. By incorporating the heuristic knowledge obtained from these classical rules, we design an algorithm FNphasing based on the flow network model. Theoretically, the time complexity of our algorithm is $(O(n^2m+m^2))$, which is better than that of 2SNP, one of the most efficient algorithms currently. After testing the performance of FNphasing with several simulated data sets, the experimental results show that when applied on large-scale data sets, our algorithm is significantly faster than the state-of-the-art Beagle algorithm. FNphasing also achieves an equal or superior accuracy compared with other approaches.