ABSTRACT

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.

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