ATD: Statistical methods for functional metagenomics in biothreat detection

ATD：生物威胁检测中功能宏基因组学的统计方法

• 批准号: 1222592
• 负责人: Lingling An
• 金额: $ 72.3万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1222592&HistoricalAwards=false
• 关键词: ATD; Statistical; methods; functional; metagenomics

High-throughput next generation sequencing technologies provide a powerful way to detect biological threats from metagenomic samples taken directly from the environment without prior knowledge of sample composition. In the analysis of metatranscriptomic data sets, researchers can examine and compare the active gene functions and pathways in the environmental or host-associated metagenomic samples with the presence or absence of biological threat agents (organisms or viruses). This is accomplished by identifying which genes are active in a sample and characterizing which functional patterns are associated with the presence of biothreat agents. Moreover, functional analysis of metagenomes can explore how functional diversity of microbial communities correlate with important biological factors of interest including the presence of a particular threat organism and its virulence level. In this research the investigators are to build rigorous statistical models and rapid computational algorithms to define detectable signatures of biological threats based on metatranscriptomic sequencing data. In particular, they study to (1) develop a probabilistic framework for characterizing the gene content in one metatranscriptomic sample, with sequencing errors considered; (2) compare multiple metatranscriptomic samples to detect statistically significant functional patterns that are associated with a biothreat agent; (3)identify "threat marker" based on the functional patterns that are linked to the presence of a biothreat agent and its virulence level where a novel statistical approach for high-dimensional variable selection problem will be proposed; (4) develop an R software package - FunctionMeta - implementing the statistical models and computational algorithms. In addition, standalone software - FunctionSim - will be developed for generating synthesized sequencing data.Known or newly emerging infectious agents, no matter whether they occurs naturally or dispersed intentionally, are a potential threat we might have to face in our modern and globalized society. In this research the investigators develop novel statistical and computational methodologies for rapid detection of biological threats based on metatranscriptomic sequencing data. Moreover, the algorithms are applicable for other functional metagenomics studies. Both the R software package and the sequence simulator tool will be made publicly available for the research community. Besides training graduate students and postdoc in the cutting-edge statistical and interdisciplinary research, the project will develop an online teaching module (posted as a series of University iTunes videos) for high school students to have an opportunity to learn the new science of metagenomics and its applications in forensics and environmental biology with emphasis of statistics in biological and health science research.

高通量下一代测序技术提供了一种强大的方法来检测直接从环境中提取的宏基因组样品的生物威胁，而无需事先了解样品组成。在对元基因组数据集的分析中，研究人员可以检查和比较环境或宿主相关元基因组样本中存在或不存在生物威胁因子（生物体或病毒）的活性基因功能和途径。这是通过确定样本中哪些基因是活跃的，并确定哪些功能模式与生物威胁剂的存在有关来实现的。此外，宏基因组的功能分析可以探索微生物群落的功能多样性如何与重要的生物因素相关，包括特定威胁生物的存在及其毒力水平。在这项研究中，研究人员将建立严格的统计模型和快速计算算法，以定义基于亚转录组测序数据的生物威胁的可检测特征。特别是，他们研究(1)开发一个概率框架，用于表征一个亚转录组样本中的基因含量，考虑到测序错误；(2)比较多个亚转录组学样本，以检测与生物威胁剂相关的统计上显著的功能模式；(3)根据与生物威胁因子存在及其毒力水平相关的功能模式识别“威胁标记”，并提出一种新的高维变量选择问题的统计方法；(4)开发R软件包FunctionMeta，实现统计模型和计算算法。此外，将开发用于生成合成测序数据的独立软件FunctionSim。已知的或新出现的传染性病原体，无论它们是自然发生的还是故意传播的，都是我们在现代和全球化社会中可能不得不面对的潜在威胁。在这项研究中，研究人员开发了新的统计和计算方法，用于基于亚转录组测序数据的生物威胁的快速检测。此外，该算法也适用于其他功能宏基因组学研究。R软件包和序列模拟器工具都将公开提供给研究界。除了在前沿统计学和跨学科研究方面培养研究生和博士后外，该项目还将开发一个在线教学模块（以大学iTunes系列视频的形式发布），让高中生有机会学习元基因组学这门新科学及其在法医学和环境生物学中的应用，重点是统计学在生物和健康科学研究中的应用。