Distance Indexing and Seed Clustering in Sequence Graphs

Abstract

Graph representations of genomes are capable of expressing more genetic variation and can therefore better represent a population than standard linear genomes. However, due to the greater complexity of genome graphs relative to linear genomes, some functions that are trivial on linear genomes become more difficult in genome graphs. Calculating distance is one such function that is simple in a linear genome but much more complicated in a graph context. In read mapping algorithms, distance calculations are commonly used in a clustering step to determine if seed alignments could belong to the same mapping. Clustering algorithms are a bottleneck for some mapping algorithms due to the cost of repeated distance calculations. We have developed an algorithm for quickly calculating the minimum distance between positions on a sequence graph using a minimum distance index. We have also developed an algorithm that uses the distance index to cluster seeds on a graph. We demonstrate that our implementations of these algorithms are efficient and practical to use for mapping algorithms.

Haplotype-aware pantranscriptome analyses using spliced pangenome graphs

“…The clustering algorithm in vg mpmap is built around the distance index described in [71]. In brief, this index can query the minimum distance between two positions in the pangenome graph by expressing the distance as the sum of a small number of precomputed distances.…”

“…Edges that are much longer than the read length are not added; this avoids treating distal elements on the same chromosome as part of the same cluster. In addition, we accelerate this process using Algorithm 3 from [71], which partitions seeds into equivalence classes based on the distance between them. The equivalence relation is the transitive closure of the relation of being connected by a path of length less than d , which is a tunable parameter.…”

SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping

“…Given the additional complexities and overheads of processing a genome graph instead of a linear reference genome, graphbased analysis exacerbates the bottlenecks of read-to-reference mapping. Due to the nascent nature of sequence-to-graph mapping, a much smaller number of software tools (and no hardware accelerators) exist for sequence-to-graph mapping [36,54,61,66,[77][78][79][80][81][82][83] compared to the traditional sequence-to-sequence mapping.…”