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: 1223057
• 负责人: Frederick Matsen
• 金额: $ 49.67万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1223057&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（“分簇”方法）；虽然这些方法很有用，但它们有以下限制。组装方法需要读取重叠，因此只能重建混合样品中最丰富的生物体。BLAST等序列相似性方法不能将reads与祖先生物联系起来，也不能表明突变的进化意义。分类学方法过于粗糙，无法反映可能表征生物威胁的细微DNA序列变化。研究人员建议通过发展以下方法的理论基础来克服这些限制：重建环境样本中DNA的细胞区隔化，即使读取计数很小，通过与现有基因组进行系统发育比较，检测合成基因组和在宏基因组样本中定向进化的证据，检测遗传物质的异常组合，因为它们的位置或观察时间，从统计学上区分微生物群落组成的有意义的变化。即使这些变化发生的水平低于使用现有方法可检测到的水平。基因工程的工具掌握在许多国家的科学家手中；这些工具可以用来合成生物武器。防止这些武器造成的伤亡取决于它们的迅速发现和识别。虽然高通量DNA测序可用于监测生物威胁，但目前可用的分析其产生的丰富信息的工具不足以统计分析威胁风险。基于进化信号统计分析的生物防御监测方法可以直接从“宏基因组”数据（来自环境样本的高通量鸟枪测序数据）中发现遗传异常和威胁。