III: Medium: Algorithms and Software Tools for Epigenetics Research

III：媒介：表观遗传学研究的算法和软件工具

• 批准号: 1302134
• 负责人: Stefano Lonardi
• 金额: $ 99.44万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302134&HistoricalAwards=false
• 关键词: III; Medium; Algorithms; Software; Tools

This project will develop a new computational framework to advance the understanding of epigenetic gene regulation in the human malaria parasite. Epigenetics is the study of heritable changes in gene expression or cellular phenotype caused by mechanisms other than changes in the underlying DNA sequence.At the core of the computational framework is the ability to solve a set of hard computational questions, which are the focus of the research plan. The computational challenges require the study of novel combinatorial optimization problems, the development of new time- and space-efficient algorithms, and ultimately the implementation and deployment of user-friendly web-based software tools. The ability to analyze the epigenome of the human malaria parasite will improve our comprehension of its biology and possibly enable molecular biologists to identify new antimalarial strategies. The proposed computational framework will also enable life scientists to make novel epigenetic discoveries and ultimately improve the understanding of the complex mechanisms that drive gene expression inother eukaryotic organisms. Software tools will be placed into the public domain, which will benefit researchers and the public worldwide, and potentially lead to new international and industrial collaborations. This project will support two graduate students and one post-doc in a highly interdisciplinary environment.Most eukaryotic genomes have a second layer of information which is embedded on chemical marks added to DNA and to the protruding tail of special proteins that package DNA into a complex called the nucleosome. One of the most astonishing discoveries in molecular biology of the past decades is that this "covert" layer, called the epigenome, affects a variety of cellular and metabolic processes. Epigenetic marks not only controls what genes are accessible in each type of cell, but also determine when the accessible genes may be activated. Molecular biologists have also confirmed that the epigenome is affected by the interactions of the organism with the environment and that changes to the epigenetic marks induced by these interactions are inherited across cell division, despite not being encoded directly in DNA. This project will study a set of computational challenges that will be brought about by the increasing number of epigenome projects. Specifically, the goal is to develop methods and software tools for (1) the analysis nucleosome and methylation maps(using a modified Gaussian mixture model and expectation maximization); (2) the study of dynamics of nucleosome positioning, histone tail modifications and DNA methylation patterns (using graph theoretical approaches, e.g., k-partite matching); (3) the analysis of DNA motifs for stable nucleosomes and specific histone modifications (using combinatorial optimization approaches); (4) the discovery of new genes using nucleosome or methylation landscapes (using machine learning classifiers); (5) the identification of statistically significant genome-wide correlations between nucleosome positioning, histone modifications, DNA methylation patterns and gene expression (using dynamic Bayesian networks). These five computational tasks will require the study of novel combinatorial optimization and machine learning problems, the development of new time- and space-efficient algorithms, and ultimately the implementation and deployment of user-friendly web-based software tools.The "platform" on which the algorithms will be developed is P. falciparum, the parasite responsible each year for 350-500 million cases of malaria, and between one and three million of human deaths world-wide. There is no vaccine against malaria (one is currently on clinical trials) and the parasite is developing resistances to almost all drugs currently available. The methods and tools developed will not be malaria-specific, and will scale to a variety of other eukaryota with much larger/complex genomes.Updates and additional information about this project will be made available at http://www.cs.ucr.edu/~stelo/iis13.htm

该项目将开发一个新的计算框架，以促进对人类疟疾寄生虫表观遗传基因调控的理解。表观遗传学是研究由潜在DNA序列变化以外的机制引起的基因表达或细胞表型的可遗传变化。计算框架的核心是解决一组困难的计算问题的能力，这是研究计划的重点。计算挑战要求研究新的组合优化问题，开发新的时间和空间高效的算法，并最终实现和部署用户友好的基于Web的软件工具。分析人类疟疾寄生虫表观基因组的能力将提高我们对其生物学的理解，并可能使分子生物学家能够确定新的抗疟疾策略。拟议的计算框架还将使生命科学家能够做出新的表观遗传学发现，并最终提高对驱动其他真核生物基因表达的复杂机制的理解。软件工具将被置于公共领域，这将使世界各地的研究人员和公众受益，并可能导致新的国际和行业合作。这个项目将在一个高度跨学科的环境中支持两名研究生和一名博士后。大多数真核基因组都有第二层信息，这些信息嵌入在DNA上的化学标记上，以及特殊蛋白质的突出尾巴上，这些蛋白质将DNA包装成一种称为核小体的复合体。过去几十年来分子生物学中最惊人的发现之一是，这种被称为表观基因组的“隐蔽层”影响着各种细胞和新陈代谢过程。表观遗传标记不仅控制在每种类型的细胞中哪些基因是可访问的，而且还决定了可访问基因何时可能被激活。分子生物学家还证实，表观基因组受到有机体与环境相互作用的影响，由这些相互作用引起的表观遗传标记的变化在细胞分裂过程中是遗传的，尽管不是直接在DNA中编码。这个项目将研究表观基因组计划数量的增加将带来的一系列计算挑战。具体地说，目标是开发用于以下方面的方法和软件工具：(1)分析核小体和甲基化图谱(使用改进的高斯混合模型和期望最大化)；(2)研究核小体定位、组蛋白尾部修饰和DNA甲基化模式的动态(使用图论方法，例如k-部分匹配)；(3)分析稳定核小体和特定组蛋白修饰的DNA基序(使用组合优化方法)；(4)利用核小体或甲基化环境发现新基因(使用机器学习分类器)；(5)确定核小体定位、组蛋白修饰、DNA甲基化模式和基因表达之间具有统计学意义的全基因组相关性(使用动态贝叶斯网络)。这五项计算任务将需要研究新的组合优化和机器学习问题，开发新的节省时间和空间的算法，并最终实施和部署用户友好的基于网络的软件工具。将在其上开发算法的“平台”是恶性疟原虫，这种寄生虫每年导致3.5亿至5亿疟疾病例，全球100万至300万人死亡。目前还没有针对疟疾的疫苗(其中一种目前正在进行临床试验)，这种寄生虫对目前几乎所有可用的药物都产生了抗药性。开发的方法和工具将不是特定于疟疾的，而是将扩展到具有更大/复杂基因的各种其他真核生物。有关该项目的更新和更多信息将在http://www.cs.ucr.edu/~stelo/iis13.htm上提供