inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains

Coexisting microbial cells of the same species often exhibit genetic variation that can affect phenotypes ranging from nutrient preference to pathogenicity. Here we present inStrain, a program that uses metagenomic paired reads to profile intra-population genetic diversity (microdiversity) across whole genomes and compares microbial populations in a microdiversity-aware manner, greatly increasing the accuracy of genomic comparisons when benchmarked against existing methods. We use inStrain to profile >1,000 fecal metagenomes from newborn premature infants and find that siblings share significantly more strains than unrelated infants, although identical twins share no more strains than fraternal siblings. Infants born by cesarean section harbor Klebsiella with significantly higher nucleotide diversity than infants delivered vaginally, potentially reflecting acquisition from hospital rather than maternal microbiomes. Genomic loci that show diversity in individual infants include variants found between other infants, possibly reflecting inoculation from diverse hospital-associated sources. inStrain can be applied to any metagenomic dataset for microdiversity analysis and rigorous strain comparison.

Automated summary

The program inStrain is used to study genetic diversity in microbial populations, particularly in fecal metagenomes of newborn premature infants. Results show that siblings share more microbial strains compared to unrelated infants, and infants born via cesarean section harbor bacteria with higher nucleotide diversity than vaginally delivered infants, potentially due to hospital acquisition. InStrain can be applied to analyze microdiversity and strain comparison in any metagenomic dataset.