Accurate Computational Detection of Influenza Reassortments

描述（由申请方提供）：流感病毒在人群中流行，每年导致显著的发病率和死亡率。产生这种抗原性新颖的流感毒株的主要机制之一是重配，其中来自两种不同毒株的病毒片段联合收割机组合.重组是病毒进化的一个重要特征，在人和禽流感病毒分离株中频繁发生。最近可用的流感序列数据的爆炸使得能够快速准确地通过计算检测流感病毒的传播成为迫切需要。通过这样做，我们将能够快速识别新的、潜在有害的菌株。我们还将更好地理解重组是如何发生的，以及为什么某些重组比其他重组在进化上更成功。 我们提出（目的1）验证和改进一种新的计算方法，用于准确检测流感病毒。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，考虑到流感片段的估计进化历史中的不确定性。所提出的方法允许分配一个置信度得分的每个重配事件，这是不可能与其他方法。我们建议验证的方法对人类和鸟类基因组的集合，也广泛的模拟数据。为了进一步提高方法的准确性，我们提出了几个扩展的基础上新的统计方法，评估分离株之间的进化距离的变化。一个成果将是一个独立的软件包。我们还建议（目标2）计算构建一个大的目录，涉及测序分离株的重组，并使用该目录来估计重组的频率和特点。特别是，我们将寻找序列突变，往往发生在同时与restructures。通过更准确地检测这些重配事件，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。 公共卫生相关性：我们建议研究新的计算方法来预测流感病毒，一种重要的人类病原体的演变中的关键事件。通过更准确地检测这些重配事件，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.