A Practical Algorithm for Optimal Inference of Haplotypes from Diploid Populations
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
An approximation algorithm for haplotype inference by maximum parsimony
Proceedings of the 2005 ACM symposium on Applied computing
Haplotype Phasing Using Semidefinite Programming
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Integer Programming Approaches to Haplotype Inference by Pure Parsimony
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Islands of Tractability for Parsimony Haplotyping
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Haplotyping Populations by Pure Parsimony: Complexity of Exact and Approximation Algorithms
INFORMS Journal on Computing
Haplotype inference by pure Parsimony
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
SAT in bioinformatics: making the case with haplotype inference
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
A polynomial case of the parsimony haplotyping problem
Operations Research Letters
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We introduce an exact algorithm, based on integer linear programming (ILP), for the parsimony haplotyping problem (PHP). The PHP uses molecular data and is aimed at the determination of a smallest set of haplotypes that explain a given set of genotypes. Our approach is based on a set-covering formulation of the problem, solved by branch and bound with both column and row generation. Existing ILP methods for the PHP suffer from the large size of the solution space, when the genotypes are long and with many heterozygous sites. Our approach, on the other hand, is based on an effective implicit representation of the solution space, and allows the solution of both real data and simulated instances, which are very hard to solve for other ILPs.