Genome scale resolution for linkage mapping

摘要 恶性疟原虫对抗疟疾青蒿素的抗药性威胁到在 在过去的十年里控制疟疾感染。我们对糖尿病的遗传决定因素缺乏全面的了解。 耐药性，以及导致耐药性突变的出现如何影响P. 恶性疟疾寄生虫。实验遗传杂交是鉴定基因的一种极其精确的手段。 耐药性的决定因素，以及一个强大的框架，以检查在 遗传、转录、蛋白质组和代谢组学水平。通过执行一系列相互关联的基因 在一个突破性的人源化小鼠模型中，我们将询问青蒿素的系统生物学 抵抗。基因组学核心将通过为本P01(广义结合 基因组学、转录组学、蛋白质组学和代谢组学)。我们将对每一个进行全基因组测序 在遗传杂交的后代中，对这种真核生物23兆碱基的基因组进行短读写 病原体识别用于检测恶性疟原虫遗传模式的遗传标记(RP01)，以及 耐药表型的QTL分析(RP02)。此外，我们将执行时间 表达谱和代谢物水平的过程分析以确定表达QTL和代谢物QTL， 和蛋白质组图谱以确定蛋白质QTL(RP03)。这些不同的技术中的每一种都具有独立的 这是一种缺陷，需要高度专业化的技能才能生成可靠的数据。我们已经组建了一支专家团队 基因组和转录组测序、蛋白质组学和代谢组学。该团队将协作解决 描述整个“组学”领域的疟疾寄生虫的巨大技术挑战。要翻译这些内容 将技术转化为可供P01调查人员和整个疟疾社区使用的成果， 我们将与数据集成和分析核心(核心B)密切合作，以生成“分析就绪型” 数据库包含每一个的表型、遗传、转录组、蛋白质组和代谢数据 子代，并随身携带基本的元数据和详细的工作流，以便在整个过程中实现强大的信息流 这个项目。

ABSTRACT Plasmodium falciparum resistance to the antimalarial artemisinin threatens the great steps forward made in the last decade to control malaria infection. We lack a comprehensive understanding on the genetic determinants of drug resistance, and how the emergence of resistance conferring mutations impacts the cellular state of the P. falciparum malaria parasite. Experimental genetic crosses are an extremely precise means to identify the genetic determinants of resistance, and a powerful framework to examine the consequences of acquiring resistance at a genetic, transcriptional, proteomic and metabolomic level. By performing a series of inter-related genetic crosses in a groundbreaking humanized mouse model we will interrogate the systems biology of artemisinin resistance. The Genomics Core will further this goal by generating `omics data for this P01 (broadly combining genomics, transcriptomics, proteomics and metabolomics). We will perform whole genome sequencing of each of the progeny of the genetic crosses, mapping short reads to the 23 megabase genome of this eukaryotic pathogen to identify genetic markers for examining patterns of inheritance in P. falciparum (RP01), and quantitative trait loci (QTL) analysis of drug resistance phenotypes (RP02). Additionally, we will perform time course analyses of expression profiles and metabolite levels to identify expression QTLs and metabolite QTLs, and proteomic profiling to identify protein QTLs (RP03). Each of these disparate technologies has independent pitfalls and require highly specialized skills to generate reliable data. We have assembled a team of experts in genome and transcriptome sequencing, proteomics and metabolomics. This team will collaboratively tackle the formidable technical challenge of profiling malaria parasites across the `omics landscape. To translate these technologies into a usable output for the investigators across the P01, and for the malaria community at large, we will work closely with the Data Integration and Analysis Core (Core B) to generate an “analysis-ready” database containing the phenotypic, genetic, transcriptomic, proteomic and metabolic data for each of the progeny, and carry with it essential metadata and detailed workflows for robust flow of information throughout the project.