Collaborative Research: ATD: Integrated statistical algorithms with ultra-high performance computing for discovering SNPs from massive next-generation metagenomic sequencing data

合作研究：ATD：将统计算法与超高性能计算相结合，用于从大量下一代宏基因组测序数据中发现 SNP

• 批准号: 1440037
• 负责人: Ping Ma
• 金额: $ 26.33万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1440037&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; Integrated; statistical

Recently, the emerging new field of metagenomics facilitated by the advent of next-generation sequencing technology enables genome sequencing of unculturable and often unknown microbes in natural environments, offering researchers an unprecedented opportunity to delineate bio-diversity of any microbial organism. Mining single nucleotide polymorphisms (SNPs) from metagenomic sequencing data offers an unique opportunity to rapidly and accurately detect known or novel strains related to multiple biothreat agents. While the sequencing technologies are evolving at unprecedented speed, researchers engaged in this enterprise are facing major computational, algorithmic and statistical challenges in the analysis of the massive metagenomic data. It is clear that both current analytical and computational methods are inadequate for this challenge. In this project, the investigator and his colleagues develop a family of statistically sound and computationally efficient algorithms to detect SNPs from metagenomic data to characterize microbial diversity in natural environments. The proposed project provides the national security and biodefense agencies new tools for rapid and accurate detection of biothreat agents. It also provides researchers in microbiology with new tools for producing abundant, high throughput SNPs for detailed analysis of the genetic basis of microbial diversity and evolution. Since this informatics tool can be used to study a wide variety of microbial communities, it helps accelerating scientific advancements of our knowledge in microbiology and evolution. The multidisciplinary nature of the project will promote collaboration between biologists, computer scientists and statistician. The multidisciplinary nature of the project will also provide postdoctoral fellows and graduate students training in statistics, genomics and scientific computing through hands-on experience.

最近，新一代测序技术的出现推动了新兴的宏基因组学领域，能够对自然环境中不可培养且通常未知的微生物进行基因组测序，为研究人员提供了前所未有的机会来描绘任何微生物的生物多样性。从宏基因组测序数据中挖掘单核苷酸多态性 (SNP) 为快速准确地检测与多种生物威胁因子相关的已知或新型菌株提供了独特的机会。虽然测序技术正在以前所未有的速度发展，但从事这一事业的研究人员在分析海量宏基因组数据时面临着重大的计算、算法和统计挑战。显然，当前的分析和计算方法都不足以应对这一挑战。在这个项目中，研究人员和他的同事开发了一系列统计上合理且计算高效的算法，用于从宏基因组数据中检测 SNP，以表征自然环境中的微生物多样性。拟议的项目为国家安全和生物防御机构提供了快速准确检测生物威胁因子的新工具。它还为微生物学研究人员提供了产生丰富、高通量 SNP 的新工具，用于详细分析微生物多样性和进化的遗传基础。由于这种信息学工具可用于研究各种微生物群落，因此它有助于加速我们在微生物学和进化方面知识的科学进步。该项目的多学科性质将促进生物学家、计算机科学家和统计学家之间的合作。该项目的多学科性质还将通过实践经验为博士后研究员和研究生提供统计学、基因组学和科学计算方面的培训。