A faster haplotyping algorithm based on block partition, and greedy ligation strategy

Authors:
Xiaohui Yao;Yun Xu;Jiaoyun Yang
Affiliations:
Department of Computer Science, University of Science and Technology of China, Hefei, Anhui, China;Department of Computer Science, University of Science and Technology of China, Hefei, Anhui, China;Department of Computer Science, University of Science and Technology of China, Hefei, Anhui, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

Citing 3
Cited 0

Haplotyping Populations by Pure Parsimony: Complexity of Exact and Approximation Algorithms

INFORMS Journal on Computing
A better block partition and ligation strategy for individual haplotyping

Bioinformatics
Haplotype inference by pure Parsimony

CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching

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Abstract

Haplotype played a very important role in the study of some disease gene and drug response tests over the past years. However, it is both time consuming and very costly to obtain haplotypes by experimental way. Therefore haplotype inference was proposed which deduce haplotypes from the genotypes through computing methods. Some genetic models were presented to solve the haplotype inference problem, and Maximum Parsimony model was one of them, but at present the methods based on this principle are either simple greedy heuristic or exact ones, which are adequate only for moderate size instances. In this paper, we presented a faster greedy algorithm named FHBPGL applying partition and ligation strategy. Theoretical analysis shows that this strategy can reduce the running time for large scale dataset and following experiments demonstrated that our algorithm gained comparable accuracy compared to exact haplotyping algorithms with less time.