ABI EAGER: New Tools for Pan-Genomics Across Phylogenetic Space

随着DNA测序质量的不断提高和成本的不断降低，基因组数据的数量继续以几何速率增加。特别是，正在积极地获得来自相同或相关物种的多个基因组；这导致了泛基因组学这一新的研究领域的出现。泛基因组学揭示了生物体如何适应环境以及在相关物种中哪些基因组特征不同或保持不变。本项目处理与高效存储和查询大型泛基因组数据集相关的一些计算挑战。新的和增强的数据结构将被开发，以有效地描述密切相关的系或物种的基因组，并允许快速的信息检索，如序列搜索等常见活动。研究人员将开发软件工具，这些工具将免费提供给一般科学界，并使用几个重要的物种来测试和验证这些工具，包括酿酒酵母（一种酵母）、拟南芥（一种小型开花植物）和苜蓿（一种小型豆科植物）。该项目将启动下一代生物信息学算法的工作，该算法可以利用来自多个数据库的增加的信息内容，并智能地使用数据进行无偏见的、物种范围的分析。下一代测序技术的进步，包括成本的下降、读取长度和通量的增加，确保了每个物种的多个基因组在未来十年内将成为常规。这组科学家将研究对图形数据结构的改进，例如用于表示泛基因组的deBruijn图和字符串图，并开发用于查询泛基因组数据的相关算法，这些算法建立在诸如FM-index之类的数据结构之上。酿酒酵母菌、拟南芥和短截紫花苜蓿将是本研究的主要模式生物，按复杂程度排序。这些生物的多个基因组已被测序，因此它们是泛基因组研究的良好候选者。特别是，该项目将补充现有的关于M. truncatula共生关系的工作。这项工作还将阐明基因组分析中参考偏差的影响，并提供避免参考偏差的替代途径。它将提供一种实用的泛基因组采样方法，提供单个FASTA序列，最大限度地减少下游分析中的参考偏差。这项研究将导致新的软件工具，这些工具将提供给社区，并将用于吸引学生参与生物信息学研究和教育活动。

As DNA sequencing continues to improve in quality and decrease in cost, the volume of genomic data continues to increase at a geometric rate. In particular, multiple genomes from the same or related species are actively being acquired; this has lead to the new research field of pangenomics. Pangenomics sheds new light on how organisms adapt to their environment and what genomic features vary or stay the same within related species. This project deals with some of the computational challenges associated with efficiently storing and querying large pan-genomic data sets. New and enhanced data structures will be developed that efficiently describe the genomes of closely related lines or species and permit fast information retrieval for common activities such as sequence searching. The investigators will develop software tools that will be made freely available to the general scientific community and test and validate these tools using several important species, including Saccharomyces cerevisiae (a yeast species), Arapidopsis thaliana (thale cress, a small flowering plant) and Medicago truncatula (barrel clover, a small legume).This project will initiate work on a next generation of bioinformatics algorithms that can exploit the increased information content available from multiple accessions and intelligently use the data for unbiased, species-wide analyses. The current trajectory of next generation sequencing improvements, including falling costs and increased read lengths and throughput, ensure that multiple genomes per species will be routine within the next decade. The researchers will investigate improvements to graphical data structures such as deBruijn and string graphs, used to represent pan genomes and develop associated algorithms for querying pan-genomic data, building on data structures such as the FM-index. Saccharomyces cerevisiae, Arapidopsis thaliana and Medicago truncatula will be the principal model organisms for this study, listed in order of complexity. Multiple genomes of each of these organisms have been sequenced so they are good candidates for pangenomic study. In particular, this project will complement existing work on symbiotic relationships of M. truncatula. This work will also shed light on the impact of reference bias in genomic analysis and provide alternative routes to avoid it. It will provide a practical method for pan-genomic sampling that provides a single FASTA sequence that minimizes reference bias in downstream analysis. This research will lead to new software tools that will be made available to the community and will be used to engage students in bioinformatics research and educational activities.