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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Preprocessing Procedure for Haplotype Inference by Pure Parsimony
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A Preprocessing Procedure for Haplotype Inference by Pure Parsimony

机译：纯简约推断单倍型的预处理程序

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摘要

Haplotype data are especially important in the study of complex diseases since it contains more information than genotype data. However, obtaining haplotype data is technically difficult and costly. Computational methods have proved to be an effective way of inferring haplotype data from genotype data. One of these methods, the haplotype inference by pure parsimony approach (HIPP), casts the problem as an optimization problem and as such has been proved to be NP-hard. We have designed and developed a new preprocessing procedure for this problem. Our proposed algorithm works with groups of haplotypes rather than individual haplotypes. It iterates searching and deleting haplotypes that are not helpful in order to find the optimal solution. This preprocess can be coupled with any of the current solvers for the HIPP that need to preprocess the genotype data. In order to test it, we have used two state-of-the-art solvers, RTIP and GAHAP, and simulated and real HapMap data. Due to the computational time and memory reduction caused by our preprocess, problem instances that were previously unaffordable can be now efficiently solved.
机译：单倍型数据在复杂疾病的研究中尤其重要，因为它比基因型数据包含更多的信息。然而，获得单倍型数据在技术上是困难且昂贵的。计算方法已被证明是从基因型数据推断单倍型数据的有效方法。这些方法之一是通过纯简约方法（HIPP）进行的单倍型推断，将该问题归为最优化问题，因此已被证明是NP难的。我们针对此问题设计并开发了一种新的预处理程序。我们提出的算法适用于单元型组而不是单个单元型。它会迭代搜索和删除无用的单倍型，以找到最佳解决方案。该预处理可以与任何需要对基因型数据进行预处理的HIPP当前求解器结合使用。为了对其进行测试，我们使用了两个最先进的求解器：RTIP和GAHAP，以及模拟的和实际的HapMap数据。由于预处理导致的计算时间和内存减少，现在可以有效解决以前无法承受的问题实例。

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