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Updated: Oct 26, 2025

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
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STRONG: metagenomics strain resolution on assembly graphs.

Christopher Quince1,2,3, Sergey Nurk4, Sebastien Raguideau5,6

  • 1Organisms and Ecosystems, Earlham Institute, Norwich, NR4 7UZ, UK. christopher.quince@earlham.ac.uk.

Genome Biology
|July 27, 2021
PubMed
Summary

We developed STRONG, a new method to identify microbial strains from metagenome data. This tool analyzes coassembly graphs to reveal strain haplotypes and their abundances, improving metagenomic analysis.

Keywords:
Assembly graphBayesianMetagenomeMicrobial communityMicrobiomeStrains

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Metagenomic studies often struggle to resolve closely related microbial strains within complex communities.
  • Accurate strain-level resolution is crucial for understanding microbial ecology, disease dynamics, and biotechnological applications.

Purpose of the Study:

  • To introduce a novel computational framework, STrain Resolution ON assembly Graphs (STRONG), for de novo strain identification from multiple metagenome samples.
  • To enable the extraction of strain-specific information, including haplotypes and abundances, from coassembly graphs.

Main Methods:

  • STRONG performs coassembly and binning to generate metagenome-assembled genomes (MAGs).
  • It utilizes the pre-variant simplification coassembly graph to extract subgraphs and unitig coverages for single-copy core genes (SCGs).
  • A Bayesian algorithm, BayesPaths, is employed to determine strain number, haplotypes, and abundances based on SCG data.

Main Results:

  • Validation using synthetic microbial communities demonstrated STRONG's capability in strain resolution.
  • Application to an anaerobic digestor time series successfully generated strain haplotypes that correlated with long Nanopore sequencing reads.

Conclusions:

  • STRONG provides a robust method for de novo strain resolution in metagenomic datasets.
  • The framework effectively leverages coassembly graphs and a Bayesian approach to characterize strain diversity and dynamics.