CAREER: De Novo Assembly of Large Genomic Data

测序是一种获得生物体基因组信息的实验性湿实验室技术。基因组组装的计算问题是利用测序数据重建基因组的完整序列。基因组组装面临几个主要挑战，包括可扩展性、准确性和对新技术的适应性。为了应对这些挑战，该项目将开发新的装配算法并探索其理论基础。开发的算法和理论将使装配工具更具可扩展性和准确性，并将使来自新兴技术的数据装配成为可能。它们将使以前不切实际的组装项目成为可能，并允许生物学家在不需要昂贵硬件的情况下进行组装。基因组组装正在成为解决我们一些重大社会挑战的越来越重要的一步。例如，对植物基因组的研究提供了对可再生能源来源的见解，并有助于确定可以赋予寄生虫对植物抗性的基因组特征。在流行病学中，不同病原体菌株或物种的基因组之间的差异可以帮助确定疾病的地理起源。提高人类基因组组装的质量也将有助于实现BRAIN计划大挑战的目标，因为它提高了我们检测导致遗传疾病（如阿尔茨海默氏症、精神分裂症、自闭症和癫痫）变异的能力。该项目将通过提高我们组装相应基因组的能力，在解决这些重大社会挑战方面取得长足进步。由于其日益突出，对公众、学生和其他研究人员进行基因组组装的教育将是重要的。将在一份普通读者杂志上发表一系列说说性文章，以教育公众了解基因组组装在诸如医疗和隐私等社会问题中的作用。生物信息学教育将通过K-12和行业外展、教学模块的开发和广泛传播以及宾夕法尼亚州立大学新研究生课程的开发来加强。该项目通过招募代表性不足的群体和本科生的参与来增加多样性。所有的教育和研究成果都将免费提供给公众，软件将是开源的。基因组组装面临几个主要挑战，包括可扩展性、准确性和对新技术的适应性。更深入地了解装配的理论原理有可能影响装配面临的所有主要挑战。本提案的主要研究目标是开发解决这些挑战的理论、算法和软件。该提案将开发新的装配算法和工具，并探索其理论基础。子目标是开发可扩展的组装器，开发模块化组装框架，创建指导实验设计的预测模型，表征字符串和de Bruijn图之间的关系，并表征序列重叠图的结构。开发的工具将应用于生物数据。技术从哈希函数的理论，I/O和并行优化，图论和统计将被使用。在整个系统发育谱中观察到的广泛的表型多样性很大程度上归因于每个物种基因组之间的差异。在对许多物种的研究中，组装参考基因组提供了巨大的生物学洞察力，是了解其遗传、功能和进化方面的关键一步。作为该提案一部分的方法和理论将使组装工具更具可扩展性和准确性，并将使来自新兴技术的数据组装成为可能。它们将使以前不切实际的组装项目成为可能，并允许生物学家在不需要昂贵硬件的情况下进行组装。

Sequencing is an experimental wet-lab technique to obtain information about the genome of an organism. The computational problem of genome assembly is to reconstruct the full sequence of the genome from sequencing data. Genome assembly faces several major challenges, including scalability, accuracy, and adaptability to new technology. In order to tackle these challenges, this project will develop new algorithms for assembly and explore their theoretical foundations. The developed algorithms and theory will make assembly tools more scalable and accurate and will enable assembly of data from emerging technologies. They will enable previously impractical assembly projects and allow biologists to perform assembly without needing expensive hardware. Genome assembly is becoming an increasingly important step in tackling some of our major societal challenges. For example, the study of plant genomes gives insights into sources of renewable energy, and helps identify the genome characteristics that can confer parasite resistance to plants. In epidemiology, the variability between the genomes of different pathogens strains or species can help pinpoint the geographic origin of a disease. Improving the quality of the human genome assembly will also help achieve the goals of the BRAIN Initiative Grand Challenge, by improving our ability to detect variations driving genetic disorders such as Alzheimer's, schizophrenia, autism and epilepsy. This project will make strides to tackling these major societal challenges by improving our ability to assemble the corresponding genomes. Due to its increasing prominence, it will be important to educate the public, students, and other researchers about genome assembly. A series of expository articles will be published in a general audience journal to educate the public about the role of genome assembly in societal issues, such as medical treatment and privacy. Bioinformatics education will be strengthened through K-12 and industry outreach, development and broad dissemination of teaching modules, and development of a new graduate course at Penn State. This project increases diversity through recruitment of underrepresented groups and engagement of undergraduate students. All educational and research outcomes will be made available freely to the public, and the software will be developed open source.Genome assembly faces several major challenges, including scalability, accuracy, and adaptability to new technology. A deeper understanding of the theoretical principles underlying assembly has the potential to impact all the major challenges facing assembly. The main research goal of this proposal is to develop the theory, algorithms, and software to tackle these challenges. The proposal will develop new algorithms and tools for assembly and explore their theoretical foundations. The subgoals are to develop a scalable assembler, to develop a modular assembly framework, to create predictive models for guiding experimental design, to characterize the relationship between string and de Bruijn graphs, and to characterize the structure of sequencing overlap graphs. The developed tools will be applied to biological data. Techniques from the theory of hash functions, I/O and parallel optimization, graph theory and statistics will be used. The wide range of phenotypic diversity observed across the phylogenetic spectrum is largely attributed to the differences between each species' genome. In the study of many species, assembling a reference genome offers tremendous biological insight and is a crucial step toward understanding their genetic, functional, and evolutionary aspects. The methods and theory developed as part of this proposal will make assembly tools more scalable and accurate and will enable assembly of data from emerging technologies. They will enable previously impractical assembly projects and allow biologists to perform assembly without needing expensive hardware.