ArticleNature communications2024
HyLight: Strain aware assembly of low coverage metagenomes.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- StrainMake: reproducible hybrid metagenomics with MAG recovery and strain-level resolution.Bioinformatics (Oxford, England) · 2026Article
- DVOUG enables robust DNA sequence assembly and reconstruction with a dynamic, variable-order graph.Cell reports methods · 2025Article
- MetaflowX: a scalable and resource-efficient workflow for multi-strategy metagenomic analysis.Nucleic acids research · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Different strains of identical species can vary substantially in terms of their spectrum of biomedically relevant phenotypes. Reconstructing the genomes of microbial communities at the level of their strains poses significant challenges, because sequencing errors can obscure strain-specific variants. Next-generation sequencing (NGS) reads are too short to resolve complex genomic regions. Third-generation sequencing (TGS) reads, although longer, are prone to higher error rates or substantially more expensive. Limiting TGS coverage to reduce costs compromises the accuracy of the assemblies. This explains why prior approaches agree on losses in strain awareness, accuracy, tendentially excessive costs, or combinations thereof. We introduce HyLight, a metagenome assembly approach that addresses these challenges by implementing the complementary strengths of TGS and NGS data. HyLight employs strain-resolved overlap graphs (OG) to accurately reconstruct individual strains within microbial communities. Our experiments demonstrate that HyLight produces strain-aware and contiguous assemblies at minimal error content, while significantly reducing costs because utilizing low-coverage TGS data. HyLight achieves an average improvement of 19.05% in preserving strain identity and demonstrates near-complete strain awareness across diverse datasets. In summary, HyLight offers considerable advances in metagenome assembly, insofar as it delivers significantly enhanced strain awareness, contiguity, and accuracy without the typical compromises observed in existing approaches.
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Registered trials
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