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Haplotyping a Diploid Single Individual with a Fast and Accurate Enumeration Algorithm

机译:利用快速准确的枚举算法对二倍体单人进行单体型分型

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The minimum error correction (MEC) model is one of the important computational models for determining haplotype information from sequencing data, i.e., single individual single nucleotide polymorphism (SNP) haplotyping, haplotype reconstruction or haplotype assembly. Due to the NP-hardness of the model, a fast and accurate enumeration algorithm is proposed for solving it. The presented algorithm reconstructs the SNP sites of a pair of haplotypes one after another. It enumerates two kinds of SNP values, i.e., (0 1)~T and (1 0)~T, for the SNP site being reconstructed, and chooses the one with more support coming from the SNP fragments that are covering the corresponding SNP site. The experimental comparisons were conducted among the presented algorithm, the FAHR, the Fast Hare and the DGS algorithms. The results prove that our algorithm can get higher reconstruction rate than the other three algorithms.
机译:最小误差校正(MEC)模型是用于从测序数据确定单倍型信息的重要计算模型之一,即单个单个单核苷酸多态性(SNP)单倍型,单倍型重构或单倍型装配。由于该模型具有NP难点,提出了一种快速,准确的枚举算法来求解。提出的算法一个接一个地重建一对单倍型的SNP位点。它枚举了正在重建的SNP位点的两种SNP值,即(0 1)〜T和(1 0)〜T,并从覆盖相应SNP位点的SNP片段中选择一种具有更多支持的SNP值。 。在提出的算法,FAHR,Fast Hare和DGS算法之间进行了实验比较。结果证明,与其他三种算法相比,我们的算法可以获得更高的重建率。

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