WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Opportunities for Combinatorial Optimization in Computational Biology
INFORMS Journal on Computing
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
Haplotype Assembly from Weighted SNP Fragments and Related Genotype Information
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Self-organizing map approaches for the haplotype assembly problem
Mathematics and Computers in Simulation
A practical parameterized algorithm for the individual haplotyping problem MLF
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
A practical parameterised algorithm for the individual haplotyping problem mlf†
Mathematical Structures in Computer Science
Using genetic algorithm in reconstructing single individual haplotype with minimum error correction
Journal of Biomedical Informatics
Semi-supervised clustering algorithm for haplotype assembly problem based on MEC model
International Journal of Data Mining and Bioinformatics
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
A heuristic algorithm for haplotype reconstruction from aligned weighted SNP fragments
International Journal of Bioinformatics Research and Applications
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Given an assembled genome of a diploid organism the haplotype assembly problem can be formulated as retrieval of a pair of haplotypes from a set of aligned weighted SNP fragments. Known computational formulations (models) of this problem are minimum letter flips (MLF) and the weighted minimum letter flips (WMLF; Greenberg et al. (INFORMS J. Comput. 2004, 14, 211-213)). In this paper we show that the general WMLF model is NP-hard even for the gapless case. However the algorithmic solutions for selected variants of WMFL can exist and we propose a heuristic algorithm based on a dynamic clustering technique. We also introduce a new formulation of the haplotype assembly problem that we call COMPLETE WMLF (CWMLF). This model and algorithms for its implementation take into account a simultaneous presence of multiple kinds of data errors. Extensive computational experiments indicate that the algorithmic implementations of the CWMLF model achieve higher accuracy of haplotype reconstruction than the WMLF-based algorithms, which in turn appear to be more accurate than those based on MLF. n the WMLF-based algorithms, which in turn appear to be more accurate than those based on MLF.