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.

当前的全素分析工具主要基于序列相似性（组装，爆炸）和分类法（“ binning”方法）；尽管有用，但这些方法具有以下局限性。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。序列相似性方法（例如BLAST）无法将读取与祖先生物有关，也不能指示突变的进化意义。分类方法太粗糙了，无法反映可能表征生物学威胁的微妙的DNA序列变化。研究者建议通过开发方法的理论基础来克服这些局限观察，从统计学上讲，即使这些转变发生在可检测到的当前方法以下的水平下，微生物群落组成的有意义的转变也是如此。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。预防伤亡这些武器取决于它们的及时检测和识别。尽管高通量DNA测序可用于监测生物威胁，但目前可用的工具用于分析其产生的大量信息不足以统计分析威胁风险。通过对进化信号的统计分析告知的BiodeFense监测方法可以产生一种直接从“元基因组”数据中检测遗传异常和威胁的方法：高吞吐量shot弹枪测序从环境样本中数据。