ITR: Algorithmic Problems in Population-Scale Genomics

gusfield， danielcalifornia - university - davis itr: population - scale genomics中的算法问题随着基因组水平技术的广泛应用（测序、重测序、序列微阵列筛选等），在种群水平上比较序列变化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。这种向种群规模基因组学的转变引入了一系列新的计算问题，并为高影响力算法的开发和研究提供了巨大的机会。这个项目的重点是新的，关键的计算问题，出现在人口规模的基因组数据采集和分析。要解决的具体计算问题产生于正在进行的种群规模的种群水平基因组变异性调查。它将侧重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获相关生物学的问题。尽管算法技术将以理论计算机科学和离散数学为基础（结果将在这些领域引起兴趣），但成功的标准将是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；寻找和开发高效的算法；为最重要的结果实现和分发软件；并将该软件应用于人口规模的基因组数据。我们的第一个重点是与从基因型信息中计算提取单倍型信息相关的问题，以及单倍型图谱的构建和使用。该项目的更大意义在于开发强大的计算工具，供遗传学家和基因制图者使用，这将有助于更有效地确定疾病易感性和其他重要遗传特征的遗传基础。

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.