ATD Collaborative Research: New theorems and algorithms for comprehensive analysis of metagenomic data via statistical phylogenetics

ATD 协作研究：通过统计系统发育学综合分析宏基因组数据的新定理和算法

• 批准号: 1341325
• 负责人: Frederick Matsen
• 金额: $ 31.59万
• 依托单位: Fred Hutchinson Cancer Research Center
• 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: 2013-03-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1341325&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; New; theorems

Current whole-metagenome analysis tools are primarily based on sequence similarity (assembly, BLAST) and taxonomies ("binning" approaches); while useful, these approaches have the following limitations. Assembly methods require read overlap and thus only reconstruct the most abundant organisms in a mixed sample. Sequence similarity approaches such as BLAST cannot relate reads to ancestral organisms and do not indicate the evolutionary significance of mutations. Taxonomic methods are too coarse to reflect the subtle DNA sequence changes that may characterize a biological threat. The investigators propose to overcome these limitations by developing the theoretical underpinnings of methods to: reconstruct the cellular compartmentalization of DNA in environmental samples, even when read counts are small, detect synthetic genomes and evidence of directed evolution within a metagenomic sample by performing a phylogenetic comparison with extant genomes, detect combinations of genetic material that are anomalous given their location or time of observation, statistically distinguish meaningful shifts in microbial community composition from noise, even when those shifts happen at a level below that detectable using currently available methods.The tools of genetic engineering are in the hands of scientists of many countries; these tools can be used to synthesize biological weapons. Prevention of casualties from these weapons depends on their prompt detection and identification. Although high-throughput DNA sequencing could be used to monitor biological threats, the currently available tools for analyzing the wealth of information it generates are insufficient to statistically analyze threat risk. A biodefense monitoring approach informed by a statistical analysis of evolutionary signal could yield a means to detect genetic anomalies and threats directly from "metagenomic" data: high throughput shotgun sequencing data from environmental samples.

目前的全宏基因组分析工具主要基于序列相似性（组装、BLAST）和分类法（“分箱”方法）；这些方法虽然有用，但具有以下局限性。组装方法需要读取重叠，因此仅重建混合样品中最丰富的生物体。 BLAST 等序列相似性方法无法将读数与祖先生物体联系起来，也不能表明突变的进化意义。分类学方法过于粗略，无法反映可能表征生物威胁的微妙 DNA 序列变化。研究人员建议通过开发方法的理论基础来克服这些限制：重建环境样本中 DNA 的细胞区划，即使读数计数很小，通过进行系统发育比较来检测宏基因组样本中的合成基因组和定向进化的证据利用现有的基因组，检测给定其观察位置或时间的异常遗传物质的组合，从统计上区分微生物群落组成的有意义的变化与噪音，即使这些变化发生在低于可检测水平的情况下目前可用的方法。基因工程的工具掌握在许多国家的科学家手中；这些工具可用于合成生物武器。防止这些武器造成的伤亡取决于它们的及时发现和识别。尽管高通量 DNA 测序可用于监测生物威胁，但目前用于分析其生成的大量信息的工具不足以对威胁风险进行统计分析。通过进化信号统计分析提供的生物防御监测方法可以产生一种直接从“宏基因组”数据（来自环境样本的高通量鸟枪测序数据）检测遗传异常和威胁的方法。