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An alignment free network approach to analyzing highly recombinant malaria parasi

一种分析高度重组疟原虫的免对齐网络方法

基本信息

批准号：
8608551
负责人：
Caroline O'Flaherty Buckee
金额：
$ 19.4万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-02-01 至 2016-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8608551
关键词：
Accounting Affect Africa South of the Sahara Antigens Awareness Biological Biology Case Study Child Classification Collaborations Computer software Computers Computing Methodologies Data Data Set Development Disease Disease Outcome Epidemiologic Methods Epidemiology Evolution Genes Genetic Genetic Recombination Genetic Variation Genomics Genotype HIV High-Throughput Nucleotide Sequencing Human Intervention Kenya Link Malaria Maps Measures Methods Modeling Network-based Output Parasites Pathology Pathway Analysis Pattern Pharmaceutical Preparations Phenotype Phylogenetic Analysis Plasmodium falciparum Population Population Genetics Public Health Publishing Recombinants Regimen Research Personnel Science Scientist Streptococcus pneumoniae Structure System Techniques Testing Vaccine Design Vaccines Variant Work age group analytical tool base computerized tools design disease phenotype insight interdisciplinary collaboration killings novel novel strategies open source pathogen pressure public health relevance tool

项目摘要

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.

描述（由申请人提供）：许多最重要的人类病原体，包括疟疾寄生虫、艾滋病毒和肺炎球菌，其特征是通过重组产生广泛的遗传多样性。为了设计有效的疫苗和持久的药物方案，我们必须了解这种多样性与人群中疾病的流行病学动态之间的关系。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严重有限。最紧迫的问题之一是缺乏能够处理这些病原体高重组率的工具，特别是考虑到目前由高通量测序技术产生的大量遗传数据集。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、进化生物学和疟疾方面的深厚专业知识，开发了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，并以疟疾寄生虫为案例研究。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意义的“集群”，并测试它们的流行病学意义。我们的方法侧重于无比对分析方法，它自然地适应序列的重组洗牌，允许分析结构特征之间的关系