Technology Core

基因组学给传染病的研究带来了革命性的变化，也准备给临床带来革命性的变化。 通过这一技术核心的活动，我们将提供高通量的基因组测序和 分析侧重于了解宿主、病原体和微生物组的相互作用是疾病的决定因素 结果。我们将提供最先进的、大规模的、高通量的测序数据，用于分析 使用最好的基因组、转录本、元基因组、元转录本和microRNA 可用的方法和技术。我们将使用我们合并的年度排序的一部分 容量，现在是每年38TB的高质量、通过筛选的数据，以对传染性进行排序 病原体、它们的宿主和它们的媒介。持续节约成本，同时保持高质量和 将通过以下方式获得数量排序：(A)广泛使用标准作业程序(SOP) 以及(B)评价、发展和纳入增强、修改和改进，如 它们变得可用。这将包括增加新的测序平台，更有效的方案， 和更强大的方法。为了更好地对宿主/微生物混合样本进行测序，我们建议 在PacBio基因组文库和llLumina转录组文库上测试靶向浓缩系统以 度量表达式。我们将继续提高我们组装基因组/转录本、鉴定 开放阅读框架，并注释基因功能，特别是关于元基因组和 元翻译组数据。我们将提供生物信息管道来分析横跨多个 这些项目包括测量人口和社区的遗传变异。比较基因组学将 通过管道实现直系同源预测和泛基因组分析。转录组分析将 包括RNAseq数据比对和可视化、差异表达分析、异质RNAseq 分析、新的转录本鉴定和miRNAseq分析。微生物组数据的分析将包括 16S rRNA扩增分析、全基因组鸟枪法分类和转录组分析 分析。有可能具有长期价值的稳定、坚固的管道将在 云虚拟资源(Clovr)将作为易于使用的虚拟资源分发给研究社区 机器。当多种数据类型可用时，将使用Sybil和 圆圈。

Genomics has revolutionized research into infectious diseases and is poised to revolutionize the clinic. Through the activities in this technology core, we will provide high-throughput genome sequencing and analysis focused on understanding host, pathogen, and microbiome interactions as determinants of disease outcome. We will provide state-of-the-art, large-scale, high-throughput sequencing data for analysis of genomes, transcriptomes, metagenomes, metatranscriptomes, and microRNAs using the best methodologies and technologies available. We will use a portion of our combined annual sequencing capacity, which is now >38 terabases of high-quality, passed-filter data per year, to sequence infectious disease agents, their hosts, and their vectors. Continued cost savings while maintaining high quality and quantity sequencing will be obtained through: (a) the extensive use of standard operating procedures (SOPs) and (b) evaluation, development, and incorporation of enhancements, modifications and improvements as they become available. This will include the addition of new sequencing platforms, more efficient protocols, and more robust methodologies. In order to better sequence host/microbe mixed specimens, we propose to test targeted enrichment systems on PacBio genomic libraries and on lllumina transcriptome libraries to measure expression. We will continue to improve our abilities to assemble genomes/transcriptomes, identify open reading frames, and annotate gene function, particularly with respect to metagenome and metatranscriptome data. We will provide bioinformatic pipelines to analyze data types that transect multiple projects including measuring genetic variation in populations and communities. Comparative genomics will be enabled through pipelines for ortholog predictions and pan genome analyses. Transcriptome analyses will include RNAseq data alignment and visualization, differential expression analysis, heterogeneous RNAseq analysis, novel transcript identification, and miRNAseq analysis. Analysis of microbiome data will include analysis of 16S rRNA amplicons, whole metagenome shotgun classification, and metatranscriptome analysis. Stable, robust pipelines with the potential to have long lasting value will be implemented in the Cloud Virtual Resource (CloVR) to be distributed to the research community as easy-to-use virtual machines. When multiple data types are available, they will be integrated and visualized using Sybil and Circleator.