ITR: Algorithmic Problems in Population-Scale Genomics

EIA-0220154 Gusfield，DanielUniversity of California-DavisITR：人口规模基因组学中的遗传学问题现在基因组水平的技术已经广泛应用（用于测序、重测序、序列的微阵列筛选等），在群体水平上比较序列变异的梦想开始成为现实。这些比较将用于帮助确定疾病易感性的遗传基础，并将有其他用途。 这种向群体规模基因组学的转变引入了一系列新的计算问题，并为高影响力的算法开发和研究提供了巨大的机会。该项目专注于群体规模基因组数据采集和分析中出现的新颖，关键的计算问题。 具体的计算问题，以解决出现了正在进行的人口规模的调查人口水平的基因组变异。 它将专注于以前没有制定和解决的新的计算问题，以及需要额外制定以更好地捕捉相关生物学的问题。 虽然算法技术将以理论计算机科学和离散数学为基础（其结果将在这些领域引起人们的兴趣），但成功的标准将是其结果在基因组学中的最终适用性。 研究将在几个层面进行：建模和定义重要问题;寻找和开发有效的算法;为最重要的结果实施和分发软件;并将软件应用于人群规模的基因组数据。我们的第一个重点是从基因型信息中计算提取单倍型信息，以及单倍型图的构建和使用相关的问题。 该项目的更大意义将是开发强大的计算工具，供遗传学家和基因作图者使用，这将有助于更有效地确定疾病易感性和其他重要遗传性状的遗传基础。

EIA-0220154Gusfield, DanielUniversity of California-DavisITR: Algorithmic Problems in Population-Scale GenomicsNow that genomic level technologies are widely available (for sequencing, resequencing, micro-array screening of sequences etc.), the dream of comparing sequence variations at the population level is starting to become a reality. These comparisons will be used to help to identify the genetic basis of disease susceptibility, and will have additional uses. This shift to opulation-scale genomics introduces a new set of computational problems, and provides a huge opportunity for high-impact algorithm development and research.This project focuses on novel, critical computational problems that arise in population-scale genomic data acquisition and analysis. The specific computational problems to be addressed arise out of on-going population-scale investigations into population-level genomic variability. It will focus on novel computational problems that have not been previously formulated and addressed, and problems where additional formulations are needed to better capture the relevant biology. Although the algorithmic techniques will be grounded in theoretical computer science and discrete mathematics (and the results will be of interest in those fields), the standards for success will be the ultimate applicability of the results in genomics. The research will be conducted at several levels: modeling and defining important problems; finding and developing efficient algorithms; implementing and distributing software for the most important results; and applying the software on population-scale genomic data. Our first focus is on problems related to computationally extracting haplotype information from genotype information, and the construction and use of haplotype maps. The larger significance of the project will be the development of powerful computational tools for use by geneticists and gene mappers, which will help to more effectively identify the genetic bases for disease susceptibility and other important genetic traits.