Binary node clustering via contrastive learning for haplotype phasing in de novo genome assembly

Abstract

Accurate haplotype phasing is essential for high-quality genome assembly, yet de novo phasing of complex genomes without parental data remains challenging. We formulate haplotype phasing as a node clustering problem with overlapping clusters on augmented unitig graphs, where nodes represent contiguous, non-branching DNA sequence fragments and two edge types can encode sequence overlap or Hi-C proximity information. We introduce a contrastive learning framework with a custom objective function and train a graph-transformer-based model, termed grapHiC, to phase paternal, maternal, and homozygous unitig nodes. grapHiC; is the first machine-learning-based method to perform reference-free haplotype phasing and the first approach to directly phase raw unitig graphs without prior simplification. We show that grapHiCaccurately clusters nodes on human-genome-scale graphs and that its predictions can effectively guide phased de novo genome assembly, producing human assemblies with contiguity and phasing quality comparable to the state of the art when integrated with the DipGNNome assembler.