Reference-Guided Chromosome-by-Chromosome de novo Assembly at Scale Using Low-Coverage High-Fidelity Long-Reads with...

Population genomics using short-read resequencing captures single-nucleotide polymorphisms and small insertions and deletions but struggles with structural variants, leading to a loss of heritability in genome-wide association studies. In recent years, long-read sequencing has improved pangenome construction for diverse eukaryotic species, including humans, crops, and other organisms of ecological and economic importance, addressing this issue to some extent. Sufficient-coverage high-fidelity data for population genomics is often prohibitively expensive, limiting its use in large-scale populations and broader eukaryotic species and creating an urgent need for robust low-coverage assemblies. However, current assemblers underperform in such conditions. To address this, HiFiCCL is proposed, the first assembly framework specifically designed for low-coverage high-fidelity reads, using a reference-guided, chromosome-by-chromosome assembly approach. This study demonstrates that HiFiCCL improves low-coverage assembly performance of existing assemblers and outperforms the state-of-the-art assemblers on human and plant datasets. Tested on 45 human datasets (∼5× coverage), HiFiCCL combined with hifiasm reduces the length of misassembled contigs relative to hifiasm by an average of 21.19% and up to 38.58%. These improved assemblies excel in detecting large germline structural variants, minimize inter-chromosome mis-scaffolding, and improve the detection of specific germline and tumor somatic structural variants based on the pangenome graph.