ATD: DEEP SEQUENCING OF MICROBIAL POPULATIONS: DISENTANGLING DIVERSITY, DYNAMICS, AND ERRORS

ATD：微生物群体的深度测序：解开多样性、动态和错误

• 批准号: 1120699
• 负责人: Daniel Fisher
• 金额: $ 74.41万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1120699&HistoricalAwards=false
• 关键词: ATD; DEEP; SEQUENCING; MICROBIAL; POPULATIONS; ATD; DEEP; SEQUENCING; MICROBIAL; POPULATIONS

Deep sequencing of microbial populations is a potentially powerful probe of their diversity and their dynamical evolutionary history. Unfortunately, the ability to analyze deep sequencing data and to infer information in the presence of errors has far from kept up with DNA sequencing capabilities. The difficulties are particularly pronounced for investigating the fine-scale diversity in a population of closely related microbes. The investigator and his colleagues will develop new algorithms for extraction of reliable information on the genomic diversity of microbial populations and analyze the short-term dynamical evolutionary processes that can generate such diversity. The algorithms will be based on modeling the processes that produce errors and biases in deep sequencing data, primarily the PCR amplification of the DNA and the sequencing itself. But in order to make useful inferences about fine-scale diversity, far better understanding is needed of the evolutionary dynamics of large microbial populations. Thus a spectrum of evolutionary scenarios will be modeled and analyzed focussing on the diversity and clues it may give to the evolutionary history. The algorithms developed for disentangling the diversity from errors will then be focussed and adapted to use the expectations from the evolutionary modeling as prior information and thereby distinguish between different scenarios. This will include developing optimized strategies for depth, breadth, and timing of DNA sequencing.Evolution of animals is usually very slow, but bacteria and viruses evolve extremely fast and this evolution leads to major threats to humans. For example, the evolution of usually innocuous bacteria within children with cystic fibrosis is what eventually leads to their premature death, and, on a global scale, evolution of influenza is what causes new epidemics. Better understanding and observations of evolution of pathogens is sorely needed. In the laboratory, bacteria and viruses also evolve --- and this evolution can be directed. Although artificial evolution can lead to many benefits, such as bacteria that eat pollutants, it can also be used for nefarious purposes. A crucial capability, such as for investigation of the anthrax attacks ten years ago, is to determine the evolutionary history from samples: when, where, and how they evolved. Fortunately, DNA sequencing has become so inexpensive that one can not only sequence many individual bacteria or viruses, but also sequence whole populations. This enables direct observations of the evolution of a population. the spectrum of differences among the individuals that provides the variation on which natural selection acts, and clues to the evolutionary history. But DNA sequencing produces many errors which make extraction of the useful information exceedingly difficult. This project will develop new algorithms for disentangling the actual DNA sequences from the errors. In parallel, sophisticated mathematical modeling will be used to explore various possible evolutionary histories and the resulting sequence variations. These will be put together to develop strategies for optimal use of DNA sequencing for inferring key aspects of the evolution of bacterial and viral populations, and understanding and predicting their consequences.

对微生物种群的深度测序是对其多样性和动态进化史的潜在强大探测。 不幸的是，在存在错误的情况下分析深度测序数据和推断信息的能力远非与DNA测序能力保持相去甚远。在研究密切相关的微生物人群中的细度多样性方面，困难特别明显。研究者及其同事将开发新算法，以提取有关微生物种群基因组多样性的可靠信息，并分析可以产生这种多样性的短期动力学进化过程。该算法将基于建模在深度测序数据中产生误差和偏见的过程，主要是DNA和测序本身的PCR扩增。但是，为了对高规模的多样性进行有用的推论，需要更好地理解大型微生物种群的进化动态。 因此，将建立和分析一系列进化场景，重点关注它可能给予进化史的多样性和线索。然后开发的算法是为了将多样性与错误脱离，然后将重点放在和适应中，以利用进化模型的期望作为先验信息，从而区分不同的情况。这将包括制定深度，广度和DNA测序时机的优化策略。动物的进化通常非常慢，但是细菌和病毒的进化非常快，这种进化会导致对人类的重大威胁。例如，囊性纤维化儿童中通常无害的细菌的演变最终导致他们的过早死亡，并且在全球范围内，流感的演变是引起新的流行病的原因。非常需要更好地理解和观察病原体的演变。 在实验室中，细菌和病毒也会进化 - 可以指向这种进化。 尽管人工进化可以带来许多好处，例如食用污染物的细菌，但也可以用于邪恶目的。至关重要的能力，例如十年前对炭疽攻击的调查，是从样本中确定进化史：何时，何地和如何发展。幸运的是，DNA测序变得如此便宜，以至于人们不仅可以序列许多单个细菌或病毒，而且可以序列整个种群。这可以直接观察人口的演变。提供自然选择行为的变化的个体之间的差异范围以及进化史的线索。 但是DNA测序会产生许多误差，从而使有用信息的提取非常困难。 该项目将开发新的算法，以将实际的DNA序列与错误分开。同时，复杂的数学建模将用于探索各种可能的进化历史和结果序列变化。这些将汇总为制定最佳使用DNA测序的策略，以推断细菌和病毒种群进化的关键方面，并理解和预测其后果。