An Allele-Centric Pan-Graph-Matrix Representation for Scalable Pangenome Analysis

TL;DR

This paper introduces an allele-centric pan-graph-matrix representation for scalable pangenome analysis, enabling efficient encoding of genetic variation and haplotype information across large cohorts.

Contribution

The authors present H1 and H2, novel representations that improve storage efficiency and explicitly encode haplotype ordering, advancing pangenome analysis methods.

Findings

Achieves near-optimal storage for genetic variants

Provides substantial compression gains for structural variants

Maintains exact information content while improving scalability

Abstract

Population-scale pangenome analysis increasingly requires representations that unify single-nucleotide and structural variation while remaining scalable across large cohorts. Existing formats are typically sequence-centric, path-centric, or sample-centric, and often obscure population structure or fail to exploit carrier sparsity. We introduce the H1 pan-graph-matrix, an allele-centric representation that encodes exact haplotype membership using adaptive per-allele compression. By treating alleles as first-class objects and selecting optimal encodings based on carrier distribution, H1 achieves near-optimal storage across both common and rare variants. We further introduce H2, a path-centric dual representation derived from the same underlying allele-haplotype incidence information that restores explicit haplotype ordering while remaining exactly equivalent in information content. Using real human genome data, we show that this representation yields substantial compression gains, particularly for structural variants, while remaining equivalent in information content to pangenome graphs. H1 provides a unified, population-aware foundation for scalable pangenome analysis and downstream applications such as rare-variant interpretation and drug discovery.