Wheeler graphs: A framework for BWT-based data structures

The famous Burrows\u2013Wheeler Transform (BWT) was originally defined for a single string but variations have been developed for sets of strings, labeled trees, de Bruijn graphs, etc. In this paper we propose a framework that includes many of these variations and that we hope will simplify the search for more. We first define Wheeler graphs and show they have a property we call path coherence. We show that if the state diagram of a finite-state automaton is a Wheeler graph then, by its path coherence, we can order the nodes such that, for any string, the nodes reachable from the initial state or states by processing that string are consecutive. This means that even if the automaton is non-deterministic, we can still store it compactly and process strings with it quickly. We then rederive several variations of the BWT by designing straightforward finite-state automata for the relevant problems and showing that their state diagrams are Wheeler graphs

A graph extension of the positional Burrows–Wheeler transform and its applications

Abstract We present a generalization of the positional Burrows–Wheeler transform, or PBWT, to genome graphs, which we call the gPBWT. A genome graph is a collapsed representation of a set of genomes described as a graph. In a genome graph, a haplotype corresponds to a restricted form of walk. The gPBWT is a compressible representation of a set of these graph-encoded haplotypes that allows for efficient subhaplotype match queries. We give efficient algorithms for gPBWT construction and query operations. As a demonstration, we use the gPBWT to quickly count the number of haplotypes consistent with random walks in a genome graph, and with the paths taken by mapped reads; results suggest that haplotype consistency information can be practically incorporated into graph-based read mappers. We estimate that with the gPBWT of the order of 100,000 diploid genomes, including all forms structural variation, could be stored and made searchable for haplotype queries using a single large compute node

Collaborative Cross Graphical Genome

Reference genomes are the foundation of most bioinformatic pipelines. They are conventionally represented as a set of single-sequence assembled contigs, referred to as linear genomes. The rapid growth of sequencing technologies has driven the advent of pangenomes that integrate multiple genome assemblies in a single representation. Graphs are commonly used in pangenome models. However, there are challenges for graph-based pangenome representations and operations. This dissertation introduces methods for reference pangenome construction, genomic feature annotation, and tools for analyzing population-scale sequence data based on a graphical pangenome model. We first develop a genome registration tool for constructing a reference pangenome model by merging multiple linear genome assemblies and annotations into a graphical genome. Secondly, we develop a graph-based coordinate framework and discuss the strategies for referring to, annotating, and comparing genomic features in a graphical pangenome model. We demonstrate that the graph coordinate system simplifies assembly and annotation updates, identifying and segmenting updated sequences in a specific genomic region. Thirdly, we develop an alignment-free method to analyze population-scale sequence data based on a pangenome model. We demonstrate the application of our methods by constructing pangenome models for a mouse genetic reference population, Collaborative Cross. The pangenome framework proposed in this dissertation simplified the maintenance and management of massive genomic data and established a novel data structure for analyzing, visualizing, and comparing genomic features in an intra-specific population.Doctor of Philosoph