Background <p>Pangenome graphs integrate multiple assemblies to represent non-redundant genetic diversity. However, current evaluations of pangenome graphs rely primarily on technical parameters (e.g., total length, number of nodes/edges, growth curves), which fail to assess how effectively the graph represents homologous stretches across the integrated assemblies and how well short reads align against pangenome graph references.</p> Results <p>We introduce a novel method to quantitatively assess how well a pangenome graph represents its integrated assemblies. Our method quantifies how many single-copy and universal k-mers from the source assemblies are uniquely and completely represented within the graph nodes. Implemented in the open-source tool PG-SCUnK, this approach identifies the fractions of unique, duplicated, and split k-mers, which correlate with short read mapping rates to the pangenome graph.</p> Conclusions <p>Insights provided by PG-SCUnK facilitate the selection of appropriate parameters to build optimal reference pangenome graphs.</p>

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PG-SCUnK: measuring pangenome graph representativeness using single-copy and universal K-mers

  • Tristan Cumer,
  • Sotiria Milia,
  • Alexander S. Leonard,
  • Hubert Pausch

摘要

Background

Pangenome graphs integrate multiple assemblies to represent non-redundant genetic diversity. However, current evaluations of pangenome graphs rely primarily on technical parameters (e.g., total length, number of nodes/edges, growth curves), which fail to assess how effectively the graph represents homologous stretches across the integrated assemblies and how well short reads align against pangenome graph references.

Results

We introduce a novel method to quantitatively assess how well a pangenome graph represents its integrated assemblies. Our method quantifies how many single-copy and universal k-mers from the source assemblies are uniquely and completely represented within the graph nodes. Implemented in the open-source tool PG-SCUnK, this approach identifies the fractions of unique, duplicated, and split k-mers, which correlate with short read mapping rates to the pangenome graph.

Conclusions

Insights provided by PG-SCUnK facilitate the selection of appropriate parameters to build optimal reference pangenome graphs.