CRII: III: RUI: Association Testing and Inversion Detection without Reference Genomes

在过去的十年里，DNA测序的成本直线下降。因此，对每个活着的有机体的基因组进行排序的潜力是我们可以掌握的。基因组组装通常需要大量的“打磨”，这通常是一个手工和劳动密集型的过程。需要新的方法来直接分析碎片化或未组装的基因组数据。对这些基因组的分析包括识别物理重排，如倒置。大规模倒置对生物体的生物学及其进化有重大影响。现有的识别倒置的计算方法主要是在经过充分研究的“参考”基因组上进行测试和开发的。该项目寻求开发新的反转检测和关联测试方法，适用于正在变得可用的大量和不断增加的片段和/或未组装的基因组。本科生研究助理将作为该项目的积极合作者得到资助。在过去的十年里，所谓的“k-mer”方法在分析未组装的基因组学或元基因组学数据方面变得流行起来。该项目寻求利用k-MERS、无监督学习和关联测试来识别零散或组合不良的群体基因组数据中的倒置。由于每个数据集将运行数百万次关联测试，因此这些方法将使用GPU进行加速。由此产生的方法和软件将与本科生研究助理一起开发，并在开放源码许可下发布。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The last decade has seen the cost of DNA sequencing plummet. Consequently, the potential to sequence the genome of every living organism is within our grasp. Genome assemblies often require significant "polishing," which is often a manual and labor-intensive process. New methods are needed to directly analyze fragmented or unassembled genomic data. Analysis of these genomes include the identification of physical rearrangements such as inversions. Large inversions have significant impacts on the biology of organisms and their evolution. Existing computational methods for identifying inversions have been primarily tested on and developed for well-studied, "reference" genomes. This project seeks to develop new inversion detection and association testing methods suitable for the large and growing number of fragmented and/or unassembled genomes that are becoming available. Undergraduate research assistants will be funded as active collaborators on the project.So-called "k-mer" methods have become popular in the last decade for the analysis of unassembled genomics or metagenomics data. This project seeks to utilize k-mers, unsupervised learning, and association testing to identify inversions in fragmented or poorly assembled population genomics data. Since millions of association tests will be run per data set, the methods will be accelerated using GPUs. The resulting method and software will be developed in conjunction with undergraduate research assistants and released under an open-source license.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.