Collaborative Research: ATD: Statistical and Computational Methods for the Analysis of Metagenomic Count Data

合作研究：ATD：宏基因组计数数据分析的统计和计算方法

• 批准号: 1220772
• 负责人: Shili Lin
• 金额: $ 26.61万
• 依托单位: Ohio State University
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1220772&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; Statistical; Computational

Molecular genetics, metagenomics, and bioinformatics are central to species/strain identification, virulence determination, pathogenicity characterization, and source attribution. Faster, cheaper sequencing technologies and the ability to sequence uncultured microbes sampled directly from their habitats have enabled the production of massive metagenomic data that are tangible for the detection of biological threats. Distilling meaningful information from millions of new genomic sequences presents serious challenges to bioinformaticians. Even though there have been intensive studies determining the taxonomical content of the sequences, there is a dearth of methods available to study the associations and interactions among metagenomic count data, human genomic data, and clinical outcomes. This project proposes to develop novel parametric and nonparametric methods for bacterial taxa identification, clinical outcome prediction, and bacterial community structure estimation. Taxa selection will be based on changes in both abundance and correlation structures. This project will also develop statistical learning methods for evaluating bacterial community dynamics and causal inference with longitudinal metagenomic data. Efficient computational methods for detecting gene-microbe interactions with integrated metagenomic and genomic data analysis will also be developed. The proposed methodologies and algorithms will be evaluated and validated with various simulation and publicly available metagenomic and genomic data. The threat of terrorists or criminal use of pathogenic organisms and their toxins remains a great concern in the United States. Bioterrorism utilizes viruses, bacteria, fungi and toxins to cause mass sickness or death of people, animals, or agriculture. The analytical methods and software developed in this proposal are anticipated to provide an important bioinformatics resource for researchers who have a goal of using metagenomic data sources for the prevention of bioterrorism and the conviction of bioterrorists. In addition, methods and software developed in this project would be a valuable contribution to environmental and human metagenomic research, which could potentially have a broader impact, especially in public health research, as a myriad of diseases such as obesity, inflammatory bowel diseases, bacterial vaginosis, and cancer all have been associated with shifts in microbiota. Finally, this project will also contribute to the training of graduate students and postdoctoral researchers in a cutting-edge interdisciplinary research area that fuses knowledge of biology, statistics and computer science.

分子遗传学、宏基因组学和生物信息学是物种/菌株鉴定、毒力测定、致病性表征和来源归属的核心。更快、更便宜的测序技术以及直接从其栖息地采样的未培养微生物测序的能力，使得能够产生大量的宏基因组数据，这些数据对于检测生物威胁是有形的。从数以百万计的新基因组序列中提取有意义的信息对生物信息学家提出了严峻的挑战。尽管已经有深入的研究确定序列的分类学内容，但缺乏可用于研究宏基因组计数数据、人类基因组数据和临床结果之间的关联和相互作用的方法。本计画拟发展新的参数与非参数方法，以进行细菌分类群鉴定、临床预后预测与细菌群落结构评估。分类群的选择将基于丰度和相关结构的变化。该项目还将开发统计学习方法，用于评估细菌群落动态和纵向宏基因组数据的因果推断。还将开发利用综合宏基因组和基因组数据分析检测基因-微生物相互作用的有效计算方法。所提出的方法和算法将进行评估和验证各种模拟和公开可用的宏基因组和基因组数据。恐怖分子的威胁或利用病原体及其毒素进行犯罪活动，仍然是美国的一个重大关切。生物恐怖主义利用病毒、细菌、真菌和毒素造成大规模疾病或人、动物或农业死亡。 该提案中开发的分析方法和软件预计将为研究人员提供重要的生物信息学资源，这些研究人员的目标是利用宏基因组数据源预防生物恐怖主义并将生物恐怖分子定罪。此外，该项目中开发的方法和软件将对环境和人类宏基因组研究做出宝贵贡献，这可能会产生更广泛的影响，特别是在公共卫生研究中，因为肥胖、炎症性肠病、细菌性阴道病和癌症等无数疾病都与微生物群的变化有关。最后，该项目还将有助于在融合生物学、统计学和计算机科学知识的尖端跨学科研究领域培养研究生和博士后研究人员。