Haplotyping as perfect phylogeny: conceptual framework and efficient solutions
Proceedings of the sixth annual international conference on Computational biology
A parsimonious tree-grow method for haplotype inference
Bioinformatics
Haplotyping Populations by Pure Parsimony: Complexity of Exact and Approximation Algorithms
INFORMS Journal on Computing
A fast haplotype inference method for large population genotype data
Computational Statistics & Data Analysis
Haplotype inference using a genetic algorithm
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Haplotype inference by pure Parsimony
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
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In this paper we present insights on the problem of haplotype inference for large genotype datasets. Our observations are drawn from an extensive comparison of three methods for haplotype inference using several datasets taken from HapMap. The methods chosen, PTG, Haplorec, and fastPHASE, are among the best known; they are based on different approaches, and are able to deal with large amounts of data. Our analysis controls the execution time and also the accuracy of results, based on the Error Rate and the Switch Error, as well as sequence conservation patterns. The results show that (1) fastPHASE and Haplorec are both more accurate than PTG, (2) fastPHASE is computationally the most expensive of the three methods, while Haplorec may fail to resolve long sequences, and (3) all approaches do better with more conserved sequences, and tend to fail in distinct sequence sites.