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降低，基因组数据量继续以几何速度增长。特别是，来自同一物种或相关物种的多个基因组正在被积极获取，这导致了泛基因组学的新的研究领域。泛基因组学揭示了生物体如何适应其环境，以及在相关物种中哪些基因组特征不同或保持不变。该项目解决了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。将开发新的和增强的数据结构，有效地描述密切相关的品系或物种的基因组，并允许为常见活动(如序列搜索)进行快速信息检索。研究人员将开发免费提供给普通科学界的软件工具，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(一种酵母物种)、刺梨(一种小型开花植物)和紫花苜蓿(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，该算法可以利用从多个物种获得的更多信息含量，并智能地使用这些数据进行公正的、全物种范围的分析。目前下一代测序改进的轨迹，包括成本下降、读取长度和吞吐量增加，确保每个物种的多个基因组将在未来十年内成为例行公事。研究人员将调查用于表示泛基因组的DeBruijn和字符串图等图形数据结构的改进，并在FM-Index等数据结构的基础上开发用于查询泛基因组数据的相关算法。酿酒酵母、刺参和紫花苜蓿将是这项研究的主要模式生物，按复杂性顺序排列。这些生物的多个基因组已经被测序，因此它们是进行泛基因组学研究的很好的候选者。特别是，该项目将补充现有关于元宝菇共生关系的工作。这项工作还将阐明参照偏差在基因组分析中的影响，并提供避免它的替代途径。它将为泛基因组采样提供一种实用的方法，提供单一的FASTA序列，最大限度地减少下游分析中的参考偏差。这项研究将产生新的软件工具，这些工具将提供给社区，并将用于让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.