UC Santa Cruz

In most sequencing experiments, sequencing reads are mapped to a reference genome assembly in order to identify the genomic elements that the reads originated from. The mapping process becomes less accurate when the sample's genome differs from the reference genome. This introduces a pervasive reference bias in which genomics analyses are systematically less accurate for non-reference alleles. In the field of pangenomics, it has been proposed that more general reference structures could mitigate reference bias. The fundamental idea is to incorporate population variation into the reference itself. The result is naturally expressible as a sequence graph. This dissertation presents the research I performed to develop methods for graph-based pangenomic analyses. First, I describe a read mapping and inference pipeline to perform haplotype-resolve transcriptomic analyses using pangenomics techniques. Next, I describe several contributions I have made to the ecosystem of pangenomic software: an interface to conventional reference methods, a software library of pangenome graph data structures, and a usable interface for indexing pangenome graphs. Finally, I describe some applications of graph theory to pangenome graphs to perform practical pangenomics tasks: identifying sites of variation and converting overlapped sequence graphs to blunt ones.