Understanding the evolution and diversity of viral pathogens using next generation sequencing technologies

动物和人类疾病的一个主要原因是病毒等感染性因素。在这个项目中，我们希望研究这些病原体的遗传物质。遗传物质被编码为核苷酸的有序“序列”。这些信息决定了病毒的生物学特性和对宿主免疫系统的反应，从而决定了兽医或医学治疗的成功，无论它们是疫苗还是药物。直到最近，病原体遗传物质还使用桑格测序来表征，这是一种在20世纪70年代末发明的技术。最近，新的测序技术已经变得可用，这些技术允许产生极大数量的序列片段，称为‘Reads’。许多人称这是测序领域的一场革命，因为它现在允许一小群研究人员处理以前只能在测序中心进行的项目，而测序中心可以处理真正大规模的测序项目，例如，对1000个人类基因组进行测序的倡议。这带来了以前所未有的规模探索病原体遗传多样性的潜力。然而，这也有不利的一面。产生的数据量超出了我们常规分析的能力，更不用说进行复杂的进化分析了。特别是当涉及病原体时，可能会生成没有合适的计算工具的数据集。这正是在这个项目的初步分析的情况下发生的事情。艾滋病毒数据的产生对了解抗药性具有重要意义，而目前还没有可用的软件。缺乏软件是因为大多数研究工作都是针对从下一代序列数据中组装单个完整的基因组。然而，对于病原体来说，有趣的问题涉及序列的多样性，即所谓的“超深”测序。因此，在这个项目中，我们建议开发对所有类型的病原体数据集普遍有用的、可靠的、易于使用的软件。这将涉及利用新技术测序平台固有的错误信息，以及我们对我们希望分析的病原体系统的大量知识。结合起来，这将使我们能够开发软件，能够总结序列样本中的变异，并对观察到的序列变化提供信心。同样重要的是，我们的基于计算机的方法将允许对数据的性质进行复杂的分析，以寻找了解病原体生物学的线索。我们将结合下一代序列数据使用该软件，以提供对RNA病毒种群宿主内动态的详细了解。当宿主内的选择性格局发生改变时，例如在个体之间传播、疾病进展或药物治疗的开始/改变之后，将特别关注基因组多样性。此外，我们的方法将普遍适用于了解遗传变异是关键的广泛研究领域。

A main cause of animal and human disease are infectious agents such as viruses. In this project we wish to study the genetic material of these pathogens. Genetic material is encoded as ordered 'sequences' of nucleotides. This information determine a virus' biological properties and response to the host immune system and thus the success of veterinary or medical treatments, whether they are vaccine or drug-based. Until very recently pathogen genetic material was characterized using Sanger sequencing, a technique invented in the late 1970s. More recently new sequencing technologies have become available that permit extremely large numbers of sequence fragments, called 'reads', to be generated. Many are referring to this as a revolution in sequencing because it now permits small groups of researchers to tackle projects previously only possible at sequencing centres, while sequencing centres can tackle truly massive sequencing projects, for example, the initiative to sequence 1,000 human genomes. This introduces the potential to explore pathogen genetic diversity on a scale that was previously unprecedented. However, there is a downside. The amount of data being generated is outstripping our ability to analyse it routinely, let alone carry out sophisticated evolutionary analysis. Particularly when it comes to pathogens, data sets could potentially be generated for which no suitable computational tools exist. This is exactly what happened in the case of the preliminary analysis in this project. HIV data was generated of importance to understanding drug resistance for which no software was available. This lack of software is because most research effort is being directed at assembling single complete genomes from next generation sequence data. However, with pathogens the interesting questions concern the diversity of sequences or so-called 'ultra-deep' sequencing. As a consequence, in this project we propose to develop, reliable, easy to use software that will be generically useful for all types of pathogen data sets. This will involve exploiting both the error information that is intrinsic to the new technology sequencing platforms and our considerable knowledge of the pathogen systems that we wish to analyse. Combined, this will permit us to develop software that will be able to summarise the variation in a sample of sequences and that will provide confidence in the sequence changes observed. Just as importantly, our computer-based approach will permit the sophisticated analysis of properties of the data in the hunt for clues to understanding a pathogen's biology. We will use this software in conjunction with next-generation sequence data to provide a detailed insight into intra-host dynamics of RNA viral populations. Particular focus will be given to genome diversity when the selective landscape within the host is altered, for example following transmission between individuals, disease progression or the initiation/alteration of drug treatments. Additionally our approach will be generically applicable to a wide range of research areas where understanding genetic variation is key.