An infrastructure for cancer virus discovery from next-generation sequencing data

描述（由申请人提供）：我们的目标是建立一个基础设施，使用我们开发的基于序列的计算减法方法，从下一代测序数据中发现与人类癌症相关的新型病毒。该项目是对ARRA研究和研究基础设施重大机遇RFA“在癌症基因组生殖系和体细胞变化的大规模研究中识别潜在病毒特征试点项目”的响应。大规模的基因组计划，包括癌症基因组图谱（TCGA）以及种系遗传与癌症相关性的研究，现在正朝着应用超高通量下一代测序方法的方向发展，无论是在cDNA水平（“RNA-seq”）还是在DNA水平，特别是全基因组测序（“WGS”）。该研究的重点是对用于病毒发现的大型下一代测序数据集进行计算分析。具体来说，我们计划建立一个基础设施，应用基于序列的计算减法（PI和合作研究者共同开发的一种方法）来评估这些大规模癌症基因组项目生成的数据库中是否存在新的非人类核酸序列。这种方法首先假设病毒诱导的癌症包含人类和病毒的核酸，并且从癌症衍生的序列中减去人类基因组将留下剩余的候选非人类和潜在的病毒序列。首先，我们将建立一个基于计算减法的数据分析和候选病原体序列发现的软件管道。其次，我们将把这个管道应用于来自TCGA和其他大规模数据集的下一代测序数据。第三，我们将通过实验测试非人类序列，我们已经确定这些序列存在于发现它们的癌症的验证队列中。第四，我们将使用验证数据来循环并提高我们的计算管道的质量。从长远来看，我们预计我们可以建立一个可持续的管道，可以作为学术或工业努力的支持。鉴定一种与人类癌症相关的新型传染因子将具有直接的预防、诊断和治疗意义。我们在这个为期两年的项目试点中开发的基础设施将通过分析数量不断增加的下一代癌症测序数据，为未来发现更多与癌症相关的病原体奠定基础。

DESCRIPTION (provided by applicant): Our goal is to build an infrastructure to discover novel viruses associated with human cancer from next-generation sequencing data, using a sequence-based computational subtraction approach that we developed. This proposed project responds to the ARRA Research and Research Infrastructure Grand Opportunities RFA on "Identifying Potential Viral Signatures in Large Scale Studies of Germline and Somatic Changes in Cancer Genomes Pilot Program". Large-scale genome projects, including The Cancer Genome Atlas (TCGA) as well as studies of germ-line genetic correlations with cancer, are now moving towards the application of ultra-high- throughput next-generation sequencing approaches, both on the cDNA level ("RNA-seq") and the DNA level, especially whole genome sequencing ("WGS"). The study is focused on the computational analysis of large next-generation sequencing data sets for virus discovery. Specifically, we plan to build an infrastructure to apply sequence-based computational subtraction, a method developed by the PI and co-investigator jointly, to evaluate the presence of novel non-human nucleic acid sequences in databases generated by these large-scale cancer genome projects. This approach starts with the assumption that virally-induced cancers contain both human and viral nucleic acids, and that subtraction of the human genome from cancer-derived sequences will leave residual candidate non-human and potentially viral sequences. First, we will build a software pipeline for computational subtraction-based data analysis and candidate pathogen sequence discovery. Second, we will apply this pipeline to the incoming flood of next-generation sequencing data from TCGA and other large-scale data sets. Third, we will experimentally test non-human sequences that we have identified for their presence in validation cohorts for the cancers in which they were discovered. Fourth, we will use the validation data to circle back and improve the quality of our computational pipeline. In the long run, we anticipate that we can build a sustainable pipeline that could be supported either as an academic or industrial effort. Identification of a novel infectious agent associated with human cancer would have immediate preventive, diagnostic and therapeutic significance. The infrastructure that we develop in this two-year project pilot will lay the groundwork for discovering additional cancer-associated pathogens in the future, by analyzing the ever-increasing quantities of next-generation cancer sequencing data. PUBLIC HEALTH RELEVANCE: Viruses are among the major causes of human cancer. Discovering these viruses can lead to major improvements in public health, because virally induced cancers can be prevented by vaccination. In recent years, hepatitis B vaccination has led to a dramatic decrease in the occurrence of liver cancer, and human papillomavirus vaccination has been shown to decrease the rates of cervical carcinoma. Genome analysis and sequencing technologies are being used to discover the causes of human cancer, in projects such as The Cancer Genome Atlas, or TCGA. These technologies can also lead to the discovery of new viruses. Therefore the National Cancer Institute is investing funds from the American Recovery and Reinvestment Act of 2009 to support the discovery of new viruses in data from cancer genome projects such as TCGA. Our proposal is responsive to the National Cancer Institute request, entitled "Identifying Potential Viral Signatures in Large Scale Studies of Germline and Somatic Changes in Cancer Genomes Pilot Program". We have developed a powerful computational approach to compare DNA and RNA sequences from cancer, or from cancer patients, to the normal human genome. Sequences that are unique to cancers, or to cancer patients, may represent novel cancer- causing viruses. In this plan, we will build a stable software infrastructure to perform this sequence comparison, apply this infrastructure to data from large-scale cancer genome projects, test candidate sequences for whether they are likely to represent viruses, and then continue to improve the software infrastructure. This effort will enable discovery of viruses by the entire cancer research community.