De Bruijn graphs for pangenomics: in-depth performance benchmarking of de Bruijn graph-based tools for read mapping

De Bruijn graphs are widely used in pangenome representation due to their numerous advantages and extensions, such as colored and compacted variants that enhance the representation of genetic variation. Although de Bruijn graphs are becoming increasingly adopted, their performance and energy impact have not been clearly studied. Such an overlooked understanding can lead to suboptimal designs for de Bruijn graph-based tools in addressing the computational challenges posed by pangenome data. To identify workflow bottlenecks and assess the efficiency of hardware utilization, we present an in-depth performance analysis of state-of-the-art de Bruijn graph-based read mapping tools on pangenomic datasets, focusing on scalability of execution time, hardware resource utilization, and energy consumption. We observe that the tools primarily prioritize data parallelism for processing read datasets, disregarding the increasing complexity of the pangenome graph, which hinders scalability. As the pangenome graph grows in size and complexity, cache miss rates also increase, leading to poor overall performance. By extensively analyzing sources of suboptimal performance, we pave the way for optimizing the existing and future tools to fully realize their potential in advancing pangenome research.