Accelerated discovery of functional non-coding genomic variation using single molecule real-time (SMRT) sequencing.

在过去的十年里，我们对基因变异在健康和疾病中的作用的理解取得了巨大的进步，这主要是由基因组测序技术的发展所推动的。包括埃克塞特大学医学院（University of Exeter Medical School）的研究人员在内的许多研究人员已经确定了与一系列衰弱性疾病和健康相关特征有关的基因，这些发现开始直接影响临床实践。然而，尽管取得了这一成功，基因组的许多区域对当代第一代和第二代测序方法仍然是隐藏的。这些包括高度复杂的重复DNA延伸和包含其他类型遗传变异（如缺失和复制）的区域。此外，由于当前方法产生的测序读数相对较短，它们不能用于检查更远端变异之间的远距离关系，也不能探索不同基因在健康和疾病中以不同版本表达的程度。最后，疾病遗传学的大多数焦点都集中在基因组中实际编码特定基因的区域，而忽略了这些区域之外发生的调节变异的作用。值得注意的是，目前的测序方法不能用于直接检测非基于序列的表观遗传修饰的存在，并探索它们在疾病中的作用。在此申请中，我们请求资金支持“第三代”测序技术的收购，这将使我们能够充分表征基因组的复杂性。我们还要求资金支持存储和分析这些复杂数据所需的高性能计算基础设施。我们的目标是通过定义人类基因组非编码区域的功能遗传和表观遗传变异，推动基因组学研究进入糖尿病、神经系统和其他医疗条件。

The last decade has seen tremendous advances in our understanding about the role of genetic variation in health and disease, driven largely by developments in technology for sequencing the genome. Genes involved in a spectrum of debilitating disorders and health-related traits have been identified, including many by researchers at the University of Exeter Medical School, and these discoveries are starting to directly impact upon clinical practice. Despite this success, however, many regions of the genome remain hidden to contemporary first- and second-generation sequencing approaches. These include highly complex stretches of repetitive DNA and regions harbouring other types of genetic variation such as deletions and duplications. Furthermore, because the sequencing reads generated by current approaches are relatively short, they cannot be used to examine the long-distance relationships between more distal variants, nor explore the extent to which different genes are expressed in different versions in health and disease. Finally, most focus in disease genetics has been on regions of the genome that actually encode specific genes, ignoring the role of regulatory variation occurring outside of these regions. Of note, current sequencing approaches cannot be used to directly detect the presence of non-sequence-based epigenetic modifications and explore their role in disease. In this application we are requesting funds to support the acquisition of "third generation" sequencing technology, which will enable us to fully characterise genomic complexity. We are also requesting funds to support the high-performance computing infrastructure needed to store and analyse this complex data. Our aim is to advance genomics research into diabetes, neurological and other medical conditions by defining functional genetic and epigenetic variation in non-coding regions of the human genome.