Sequencing Framework for the Sensitive Detection and Precise Mapping of Defective Interfering Particle-Associated Deletions across Influenza A and B Viruses
Sequencing Framework for the Sensitive Detection and Precise Mapping of Defective Interfering Particle-Associated Deletions across Influenza A and B Viruses
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DOI：
10.1128/jvi.00354-19
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发表时间：
2019-06-01
影响因子：
5.4
通讯作者：
Brooke, Christopher B.
中科院分区：
文献类型：
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作者：
Alnaji, Fadi G.;Holmes, Jessica R.;Brooke, Christopher B.
流感病毒感染过程中缺陷干扰颗粒（DIP）形成的机制和后果仍然知之甚少。下一代测序（NGS）技术的发展使得识别大量DIP相关序列成为可能，为更好地理解其生物学相关性提供了强大的工具。然而，NGS方法提出了许多技术挑战，包括在频繁突变和碱基识别错误的情况下精确识别和定位缺失连接，以及可能出现许多实验和计算伪影。在这里，我们详细介绍了一个基于Illumina的测序框架和生物信息学管道，能够生成高度准确和可重复的DIP相关连接序列谱。我们使用模拟和实验控制数据集的组合来优化流水线性能，并证明没有显着的文物。最后，我们使用这个优化的管道来揭示DIP相关连接形成的模式在不同的甲型和B型流感病毒株和亚型之间的差异，并展示这些数据如何提供对DIP形成机制的深入了解。总体而言，这项工作提供了一个详细的路线图，高分辨率的轮廓和分析DIP相关的序列在流感病毒population.IMPORTANCE流感病毒缺陷干扰颗粒（DIP），在其基因组中的港口内部缺失自然发生在人类感染和细胞培养。它们被假设影响病毒的致病性;然而，它们的具体功能仍然难以捉摸。DIP相关缺失连接的准确检测对于理解DIP生物学至关重要，但由于一系列技术问题而变得复杂，这些问题可能会使结果产生偏差或混淆。在这里，我们展示了一个相结合的实验和计算框架，用于检测DIP相关的缺失路口使用下一代测序（NGS）。我们详细介绍了如何验证管道的性能，并为有兴趣使用它的团体提供生物信息学管道。使用这个优化的管道，我们检测了数百个不同的缺失连接在感染过程中产生的各种面板的流感病毒，并使用这些数据来测试一个长期存在的假设有关的DIP形成的分子细节。
The mechanisms and consequences of defective interfering particle (DIP) formation during influenza virus infection remain poorly understood. The development of next-generation sequencing (NGS) technologies has made it possible to identify large numbers of DIP-associated sequences, providing a powerful tool to better understand their biological relevance. However, NGS approaches pose numerous technical challenges, including the precise identification and mapping of deletion junctions in the presence of frequent mutation and base-calling errors, and the potential for numerous experimental and computational artifacts. Here, we detail an Illumina-based sequencing framework and bioinformatics pipeline capable of generating highly accurate and reproducible profiles of DIP-associated junction sequences. We use a combination of simulated and experimental control data sets to optimize pipeline performance and demonstrate the absence of significant artifacts. Finally, we use this optimized pipeline to reveal how the patterns of DIP-associated junction formation differ between different strains and subtypes of influenza A and B viruses and to demonstrate how these data can provide insight into mechanisms of DIP formation. Overall, this work provides a detailed roadmap for high-resolution profiling and analysis of DIP-associated sequences within influenza virus populations.IMPORTANCE Influenza virus defective interfering particles (DIPs) that harbor internal deletions within their genomes occur naturally during infection in humans and during cell culture. They have been hypothesized to influence the pathogenicity of the virus; however, their specific function remains elusive. The accurate detection of DIP-associated deletion junctions is crucial for understanding DIP biology but is complicated by an array of technical issues that can bias or confound results. Here, we demonstrate a combined experimental and computational framework for detecting DIP-associated deletion junctions using next-generation sequencing (NGS). We detail how to validate pipeline performance and provide the bioinformatics pipeline for groups interested in using it. Using this optimized pipeline, we detect hundreds of distinct deletion junctions generated during infection with a diverse panel of influenza viruses and use these data to test a long-standing hypothesis concerning the molecular details of DIP formation.