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Phylogeny- and Parsimony-Based Haplotype Inference with Constraints

机译:基于系统发育和简约的单倍型推导

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Haplotyping, also known as haplotype phase prediction, is the problem of predicting likely haplotypes based on genotype data. One fast computational haplotyping method is based on an evolutionary model where a perfect phylogenetic tree is sought that explains the observed data. In their CPM 2009 paper, Fellows et al. studied an ex tension of this approach that incorporates prior knowledge in the form of a set of candidate haplotypes from which the right haplotypes must be chosen. While this approach may help to increase the accuracy of haplotyping methods, it was conjectured that the resulting formal problem constrained perfect phylogeny haplotyping might be NP-complete. In the present paper we present a polynomial-time algorithm for it. Our algorithmic ideas also yield new fixed-parameter algorithms for related haplotyping problems based on the maximum parsimony assumption.
机译:单倍型,也称为单倍型阶段预测,是基于基因型数据预测可能的单倍型的问题。一种快速的计算单倍型方法是基于进化模型的,其中寻找解释所观察到数据的完美系统树。 Fellows等人在其CPM 2009论文中。研究了这种方法的扩展,该方法以一组候选单倍型的形式结合了先验知识,必须从中选择合适的单倍型。尽管此方法可能有助于提高单元型方法的准确性,但可以推测,由此产生的形式问题限制了完美的系统发育单元型可能是NP完全的。在本文中,我们提出了一种多项式时间算法。我们的算法思想还基于最大简约假设为相关的单元型问题产生了新的固定参数算法。

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