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FNphasing: A Novel Fast Heuristic Algorithm for Haplotype Phasing Based on Flow Network Model
March-April 2013 (vol. 10 no. 2)
pp. 372-382
Jiaoyun Yang, University of Science and Technology of China, Hefei
Yun Xu, University of Science and Technology of China, Hefei
Xiaohui Yao, University of Science and Technology of China, Hefei
Guoliang Chen, University of Science and Technology of China, Hefei
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.
Index Terms:
Hidden Markov models,Algorithm design and analysis,Accuracy,Phylogeny,Computational biology,Bioinformatics,Data models,flow network,Hidden Markov models,Algorithm design and analysis,Accuracy,Phylogeny,Computational biology,Bioinformatics,Data models,heuristic methods,Haplotype phasing
Citation:
Jiaoyun Yang, Yun Xu, Xiaohui Yao, Guoliang Chen, "FNphasing: A Novel Fast Heuristic Algorithm for Haplotype Phasing Based on Flow Network Model," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 2, pp. 372-382, March-April 2013, doi:10.1109/TCBB.2013.18
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