Haplotyping as perfect phylogeny: conceptual framework and efficient solutions
Proceedings of the sixth annual international conference on Computational biology
Model-based inference of haplotype block variation
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Maximum likelihood resolution of multi-block genotypes
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Computational Problems in Perfect Phylogeny Haplotyping: Typing without Calling the Allele
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Parsimony-based genetic algorithm for haplotype resolution and block partitioning
Parsimony-based genetic algorithm for haplotype resolution and block partitioning
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Problems of haplotyping and block partitioning have been extensively studied with regard to the regular genotype data, but more cost-efficient data called XOR-genotypes remain under-investigated. Previous studies developed methods for haplotyping of short-sequence partial XOR-genotypes. In this paper we propose a new algorithm that performs haplotyping of long-range partial XOR-genotype data with possibility of missing entries, and in addition simultaneously finds the block structure for the given data. Our method is implemented as a fast and practical algorithm. We also investigate the effect of the percentage of fully genotyped individuals in a sample on the accuracy of results with and without the missing data. The algorithm is validated by testing on the HapMap data. Obtained results show good prediction rates both for samples with and without missing data. The accuracy of prediction of XOR sites is not significantly affected by the presence of 10% or less missing data.