CAREER: Phylogenomics - New Computational Methods through Stochastic Modeling and Analysis

职业：系统基因组学 - 通过随机建模和分析的新计算方法

• 批准号: 1149312
• 负责人: Sebastien Roch
• 金额: $ 44.44万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149312&HistoricalAwards=false
• 关键词: CAREER; Phylogenomics; New; Computational; Methods

In this project, modeling and analysis techniques from probability theory will be used to study several important computational problems in the area of phylogenomics, i.e., the integration of genome analysis and systematic studies. Various mechanisms such as hybridization events, lateral gene transfers, gene duplications and losses, and incomplete lineage sorting commonly lead to incongruences between inferred gene genealogies. As a result, one is led to consider forests of gene histories as well as more complex network representations of the evolutionary history of life. The main goals of the research are to improve large-scale likelihood-based gene tree estimation, develop computational methods to assemble species phylogenies from gene histories, and detect network-like signal in molecular data. Drawing on a combination of ideas from discrete probability, algorithms, and mathematical statistics, novel methodologies will be developed that are both statistically accurate and computationally efficient for these challenging inference problems.Biologists face major statistical and computational challenges in modeling, analyzing, and interpreting the massive genetic datasets produced by next-generation technologies, including genomic variation within populations, whole genomes from multiple species, and environmental samples. In particular, high-throughput sequencing is transforming the reconstruction of the Tree of Life, a fundamental problem in biology which provides insights into the study of evolution, adaptation, and speciation. Through the development, implementation, and broad dissemination of new practical algorithms for phylogenomic studies based on mathematical analysis, this project will help advance the state of knowledge in evolutionary biology and contribute to the numerous benefits to society of phylogenetic research. Integration of research and education is a major component of this proposal. In addition to providing training for graduate students and postdoctoral researchers, new undergraduate and graduate courses will be developed and research experiences for undergraduates will be an important part of the project.

在这个项目中，来自概率论的建模和分析技术将用于研究生物基因组学领域的几个重要计算问题，即，基因组分析和系统研究的结合。 各种机制，如杂交事件，横向基因转移，基因复制和丢失，以及不完整的谱系排序通常会导致推断的基因系谱之间的不一致。 因此，人们开始考虑基因历史的森林，以及生命进化史的更复杂的网络表示。 该研究的主要目标是改进大规模基于似然的基因树估计，开发计算方法来组装物种的基因历史，并检测分子数据中的网络信号。 利用离散概率、算法和数理统计的思想组合，将开发新的方法，这些方法在统计上准确且计算效率高，可以解决这些具有挑战性的推理问题。生物学家在建模、分析和解释下一代技术产生的大量遗传数据集方面面临着重大的统计和计算挑战，包括种群内的基因组变异，多个物种的全基因组和环境样本。 特别是，高通量测序正在改变生命之树的重建，这是生物学中的一个基本问题，为进化，适应和物种形成的研究提供了见解。 通过开发、实施和广泛传播基于数学分析的新的实用算法，该项目将有助于提高进化生物学的知识水平，并为系统发育研究的社会带来诸多好处。研究和教育的一体化是这项建议的一个主要组成部分。 除了为研究生和博士后研究人员提供培训外，还将开发新的本科生和研究生课程，本科生的研究经验将是该项目的重要组成部分。