An alignment free network approach to analyzing highly recombinant malaria parasi

描述（由申请人提供）：许多最重要的人类病原体，包括疟原虫、HIV和肺炎球菌，其特征在于通过重组产生的广泛遗传多样性。为了设计有效的疫苗和长效的药物治疗方案，我们必须了解这种多样性与人类疾病流行病学动态的关系。虽然高通量测序技术正在从这些病原体中产生大量的基因组序列数据，但能够理解它们的分析工具受到严重限制。最紧迫的问题之一是缺乏可以处理这些病原体的高重组率的工具，特别是考虑到现在通过高通量测序技术生成的大量基因数据集。该项目代表了计算机科学家和疟疾生物学家之间的跨学科合作，汇集了网络分析，计算方法，流行病学，进化生物学和疟疾的深厚专业知识，以开发一套新的可扩展的通用计算工具，用于可视化和分析重组基因序列，使用疟疾寄生虫作为案例研究。利用网络科学领域的最新进展，该项目将开发新的方法，从序列数据中准确推断“重组网络”，自动识别这些网络中具有统计学意义的“簇”，并测试其流行病学意义。我们的方法侧重于无干扰的分析方法，它自然地适应序列的重组改组，允许分析序列之间关系的结构特征。 基因，并提供了深入了解重组对其进化的影响。除了 为了回答重要的生物学和流行病学问题，该项目将产生一个新的开放源码软件平台，使研究人员能够分析各种重要的人类病原体的重组序列数据。

DESCRIPTION (provided by applicant): Many of the most important human pathogens including the malaria parasite, HIV, and the pneumococcus, are characterized by extensive genetic diversity generated by recombination. In order to design of effective vaccines and long-lasting drug regimens, it is critical that we understand how this diversity relates to the epidemiological dynamics of disease in human populations. While high throughput sequencing techniques are generating vast volumes of genomic sequence data from these pathogens, the analytical tools capable of making sense of them are severely limited. One of the most pressing problems is the lack of tools that can deal with these pathogens' high rates of recombination, particularly when considering the vast genetic datasets now being generated by high throughput sequencing techniques. This project represents an interdisciplinary collaboration between computer scientists and malaria biologists, bringing together deep expertise on network analysis, computational methods, epidemiology, evolutionary biology and malaria, to develop a new suite of scalable, general computational tools for visualizing and analyzing recombinant gene sequences, using the malaria parasite as a case study. Drawing on recent advances in the field of network science, the project will develop novel methods for accurately inferring "recombination networks" from sequence data, automatically identifying statistically significant "clusters" in these networks, and testing their epidemiological significance. Our approach focuses on alignment-free analysis methods, which naturally accommodate the recombinant shuffling of sequences, allows for the analysis of structural features of the relationships between genes, and provides insights into the effects of recombination on their evolution. In additional to answering important biological and epidemiological questions, this project will produce a novel open-source software platform that will enable researchers to analyze recombinant sequence data from a wide variety of important human pathogens.