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度增长。 特别是，来自相同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研究领域。 泛基因组学为生物如何适应环境以及相关物种的基因组特征变化或保持不变提供了新的视角。 该项目涉及与有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，以有效地描述密切相关的品系或物种的基因组，并允许快速信息检索的共同活动，如序列搜索。 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，并使用包括酿酒酵母在内的几个重要物种测试和验证这些工具。（一种酵母），Arapidopsis thaliana（塔勒水芹，一种小型开花植物）和蒺藜苜蓿（桶三叶草，一种小型豆类）。该项目将启动下一代生物信息学算法的工作，该算法可以利用多个生物信息系统中增加的信息内容。加入和智能地使用数据进行无偏见的，物种范围的分析。 当前下一代测序技术的发展轨迹，包括成本下降、读长和通量增加，确保每个物种的多个基因组将在未来十年内成为常规。 研究人员将研究deBruijn和字符串图等图形数据结构的改进，用于表示泛基因组，并开发用于查询泛基因组数据的相关算法，建立在FM索引等数据结构的基础上。 酿酒酵母、拟南芥和蒺藜苜蓿将是本研究的主要模式生物，按复杂性顺序列出。 这些生物体中的每一种的多个基因组已经被测序，因此它们是泛基因组学研究的良好候选者。 特别是，该项目将补充现有的工作共生关系的M。蒺藜 这项工作也将揭示基因组分析中参考偏倚的影响，并提供替代路线，以避免它。它将提供一个实用的方法，泛基因组采样，提供一个单一的FASTA序列，最大限度地减少下游分析中的参考偏倚。这项研究将导致新的软件工具，将提供给社区，并将用于从事生物信息学研究和教育活动的学生。

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.