Accurate Computational Detection of Influenza Reassortments

描述（由申请人提供）：流感病毒在人群中流行，每年造成显著的发病率和死亡率。产生这种具有抗原性的新型流感毒株的主要机制之一是重新分类，即来自两个不同毒株的病毒片段结合在一起。人类和禽类分离株之间经常发生重配，这是病毒进化的一个重要特征。最近可用的流感序列数据激增，迫切需要能够快速准确地通过计算检测重组。通过这样做，我们将能够快速识别新的、潜在有害的菌株。我们也将更好地理解重组是如何发生的，以及为什么某些重组在进化上比其他重组更成功。我们提出（目标1）验证和改进一种新的精确检测流感重组的计算方法。该方法通过比较系统发育树的两种分布，而不是一对可能不可靠或无信息的共识树，考虑到流感片段估计进化史中的不确定性。所提出的方法允许为每个重排事件分配置信度分数，这是其他方法无法实现的。我们建议在人类和鸟类基因组的收集以及大量的模拟数据上验证该方法。为了进一步提高方法的准确性，我们提出了几种基于新统计方法的扩展，以评估分离株之间进化距离的变化。一个结果将是一个独立的软件包。我们还建议（目标2）计算构建一个涉及测序分离的重组大目录，并使用该目录来估计重组的频率和特征。特别是，我们将寻找与重组同时发生的序列突变。通过更准确地检测这些重组事件，我们将更好地了解流感的演变。这将有助于规划疫苗接种战略和设计有效的监测方案。

DESCRIPTION (provided by applicant): The influenza virus is endemic in the human population and causes significant annual morbidity and mortality. One of the primary mechanisms for generating such antigenically novel influenza strains is re- assortment, in which viral segments from two distinct strains combine. Reassortment occurs frequently among human and avian isolates and is an important feature of the evolution of virus. The recent explosion in available influenza sequence data has made it a pressing need to be able to computationally detect reassortments quickly and accurately. By doing so, we will be able identify new, potentially harmful strains quickly. We will also gain a better understanding of how reassortment occurs and why certain reassortments are more evolutionarily successful than others. We propose to (Aim 1) validate and improve a new computational approach for the accurate detection of influenza reassortments. The method takes into account uncertainty in the estimated evolutionary histories of the influenza segments by comparing two distributions of phylogenetic trees, rather than a pair of possibly unreliable or uninformative consensus trees. The proposed method permits the assignment of a confidence score to each reassortment event, something that is not possible with other approaches. We propose to validate the method on collections of human and avian genomes and also on extensive simulated data. In order to further improve the methods accuracy, we propose several extensions based on novel statistical methods that assess the changes in evolutionary distances between isolates. One outcome will be a stand-alone software package. We also propose to (Aim 2) computationally construct a large catalog of reassortments involving the sequenced isolates and to use this catalog to estimate the frequency and characteristics of reassortments. In particular, we will look for sequence mutations that tend to occur contemporaneously with reassortments. By more accurately detecting these reassortment events, we will gain a better understanding of influenza evolution. This will help plan vaccination strategies and design effective surveillance protocols. PUBLIC HEALTH RELEVANCE: We propose to study new computational methods for predicting reassortments, a key event in the evolution of influenza virus, an important human pathogen. By more accurately detecting these reassortment events, we will gain a better understanding of influenza evolution. This will help plan vaccination strategies and design effective surveillance protocols.