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StrainFLAIR: strain-level profiling of metagenomic samples using variation graphs
Kévin Da Silva1,2, Nicolas Pons1, Magali Berland1
1Université Paris-Saclay, INRAE, MGP, Jouy-en-Josas, France.
Peerj
|September 13, 2021
Summary
StrainFLAIR utilizes variation graphs to accurately identify and quantify bacterial strains within complex metagenomic samples. This method enables precise strain-level microbiome analysis for diagnostic and therapeutic applications.
Area of Science:
- Genomics
- Bioinformatics
- Metagenomics
Background:
- Genomic studies are transitioning from linear references to pangenome and variation graphs.
- Accurate strain-level abundance estimation is crucial for microbiome research and understanding phenotype associations.
Purpose of the Study:
- To demonstrate the utility of variation graphs for strain-level genomic indexing and characterization.
- To develop and validate a method for querying variation graphs to identify unknown genomes at the strain level.
Main Methods:
- Development of StrainFLAIR, a tool leveraging variation graphs for genomic indexing.
- Testing StrainFLAIR on simulated and real metagenomic datasets containing mixtures of bacterial strains.
Main Results:
- StrainFLAIR successfully distinguished and estimated abundances of closely related strains in simulated *Escherichia coli* data.
- The tool identified and quantified a novel strain within the simulated dataset.
- StrainFLAIR accurately estimated strain abundances in a complex real-world metagenomic sample.
Conclusions:
- Variation graphs are effective for strain-level genomic analysis.
- StrainFLAIR provides a feasible approach for characterizing unknown genomes and quantifying strain abundances in metagenomic samples.
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