ATD: Statistical Methods and Software for Analyzing Massively Parallel Epigenomic Sequencing Data

ATD：用于分析大规模并行表观基因组测序数据的统计方法和软件

• 批准号: 1042946
• 负责人: Shili Lin
• 金额: $ 48.64万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1042946&HistoricalAwards=false
• 关键词: ATD; Statistical; Methods; Software; Analyzing

The dawn of the massively parallel sequencing technology has propelledresearch in epigenetics, from genome-wide methylation profiling,histone modification patterns clustering, to identifying generegulation through spatial interactions. The power of epigeneticshas already been felt, such as its use in making drugs for treatingseveral diseases, but unfortunately, it has also been recognized thatit may be exploited for the bad. The powerful, rapidly acquirable,stable, and heritable features of epigenetic could be a perfect vehiclefor bio-terrorism. The regulatory mechanism of gene expression maybe altered based on the epigenetic principle to create mutant cellswith devastating consequences very quickly. To counter the potentialof such threat, the detection of changes in epigenetic marks is akey issue. This project aims to develop likelihood based as wellas Bayesian methodology, computational algorithms and associatedsoftware for analyzing massively parallel epigenomic sequencing datathat are tangible for detection of biological threats. The focus willbe on chromatin signature and structure analysis to study histonebinding patterns in promoters and enhancers and spatial interactionsbetween promoters and enhancers within a protein complex. Methods andalgorithms developed will be implemented in three software packages:DIME, ChAPE, and BASIC. It is anticipated that the analytical toolsdeveloped will contribute to uncover global host-response epigeneticpatterns.Charles Darwin's publication of "On the origin of species" 150 yearsago has taught us that evolutionary changes take many generationsof natural selection. In recent years, however, a new scientificarea called epigenetics is helping to usher in a paradigm shift. Itis hypothesized, based on amassed scientific evidence, that powerfulenvironmental conditions may leave an imprint on the genetic material,which can lead to passage of new traits in a single generationthrough the epigenetic process. The importance of epigenetics hasbeen recognized in the scientific community, and indeed, epigeneticshas been used for the good, such as its utilization for producing drugsfor treating complex diseases. Unfortunately, it may also be exploitedby bio-terrorists. In other words, gene regulation mechanism may bealtered to create mutant cells, which could pose great threats withdevastating consequence. Anticipating the potential of such type ofthreats, this project proposes statistical methods and computationalalgorithms to analyze epigenetic data from advanced genomic sequencingtechnology to detect mutant cells that may have gone through epigeneticchanges. These tools are anticipated to contribute to early detectionof exposure to potential biowarfare pathogen. This project will alsocontribute to the training of the next generation of researchers ina cutting-edge interdisciplinary research area that fuses knowledgein biology, statistics and computer science.

大规模平行测序技术的出现推动了表观遗传学的研究，从全基因组甲基化分析、组蛋白修饰模式聚类到通过空间相互作用确定基因调控。表观遗传学的力量已经被感受到了，例如它在制造治疗多种疾病的药物中的应用，但不幸的是，它也被认识到可能被利用来做坏事。表观遗传学强大、可快速获得、稳定和可遗传的特征可能是生物恐怖主义的完美工具。基于表观遗传学原理，基因表达的调节机制可能被改变，以非常快地产生具有破坏性后果的突变细胞。为了应对这种潜在的威胁，检测表观遗传标记的变化是一个关键问题。该项目旨在开发基于可能性的贝叶斯方法，计算算法和相关软件，用于分析大规模并行表观基因组测序数据，这些数据对于检测生物威胁是有形的。重点将放在染色质特征和结构分析，以研究启动子和增强子中的组蛋白结合模式以及蛋白质复合物中启动子和增强子之间的空间相互作用。所开发的方法和算法将在三个软件包中实现：DIME、ChAPE和BASIC。可以预见，所开发的分析工具将有助于揭示全球宿主反应的表观遗传模式。查尔斯达尔文在150年前发表的《物种起源》一书告诉我们，进化的变化需要经过许多代的自然选择。然而，近年来，一个名为表观遗传学的新科学领域正在帮助引领一种范式转变。基于积累的科学证据，人们假设，强大的环境条件可能会在遗传物质上留下印记，这可能导致新的性状在一代人中通过表观遗传过程传递。表观遗传学的重要性已经在科学界得到了认可，事实上，表观遗传学已经被用于好的方面，例如利用它来生产治疗复杂疾病的药物。不幸的是，它也可能被生物恐怖分子利用。换句话说，基因调控机制可能被改变，产生突变细胞，这可能造成巨大的威胁和毁灭性的后果。预见到这类威胁的潜在性，该项目提出了统计方法和计算算法来分析来自先进基因组测序技术的表观遗传数据，以检测可能经历了表观遗传变化的突变细胞。预计这些工具将有助于早期发现潜在的生物战病原体。该项目还将有助于培养下一代研究人员，他们将生物学，统计学和计算机科学知识融合在一起。