• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Intelligent Systems for Molecular Biology; 20000816-23; La Jolla,CA(US) >A Practical Algorithm for Optimal Inference of Haplotypes from Diploid Populations
【24h】

A Practical Algorithm for Optimal Inference of Haplotypes from Diploid Populations

机译：一种从二倍体群体推算单倍型的实用算法

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The next phase of human genomics will involve large-scale screens of populations for significant DNA polymorphisms, notably single nucleotide polymorphisms (SNP's). Dense human SNP maps are currently under construction. However, the utility of those maps and screens will be limited by the fact that humans are diploid, and that it is presently difficult to get separate data on the two "copies". Hence genotype (blended) SNP data will be collected, and the desired haplotype (partitioned) data must then be (partially) inferred. A particular non-deterministic inference algorithm was proposed and studied before SNP data was available, and extensively applied more recently to study the first available SNP data. In this paper, we consider the question of whether we can obtain an efficient, deterministic variant of that method to optimize the obtained inferences. Although we have shown elsewhere that the optimization problem is NP-hard, we present here a practical approach based on (integer) linear programming. The method either returns the optimal answer, and a declaration that it is the optimal, or declares that it has failed to find the optimal. The approach works quickly and correctly, finding the optimal on all simulated data tested, data that is expected to be more demanding than realistic biological data.
机译：人类基因组学的下一阶段将涉及对人群进行大规模筛查，以发现显着的DNA多态性，尤其是单核苷酸多态性（SNP's）。密集的人类SNP地图目前正在建设中。但是，这些地图和屏幕的实用性将受到以下事实的限制：人类是二倍体，并且目前很难在两个“副本”上获得单独的数据。因此，将收集基因型（混合的）SNP数据，然后必须（部分）推断所需的单倍型（分区的）数据。在获得SNP数据之前，提出并研究了一种特定的非确定性推理算法，并且在最近广泛应用于研究第一个可用的SNP数据。在本文中，我们考虑是否可以获取该方法的有效，确定性变体来优化所获得的推论的问题。尽管我们已经在其他地方证明了优化问题是NP难题的，但是我们在这里提出了一种基于（整数）线性规划的实用方法。该方法要么返回最佳答案，然后声明它是最佳答案，要么声明未能找到最佳答案。该方法可以快速，正确地工作，可以在所有测试的模拟数据上找到最佳的方法，这些数据比实际的生物学数据要求更高。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号