David Relman lab and Dmitri Petrov lab, Stanford University

The human gut microbiome is a diverse, complex ecosystem that has important impacts on human health. Traditional genomic methods like 16S sequencing and metagenomic profiling provide genus- and species-level resolution of gut microbiome composition, but there is a growing appreciation that strains of the same species can play distinct functional roles in the gut microbiome. However, inferring the strain-level composition of the gut microbiome from metagenomic sequencing data remains a challenging bioinformatic problem. In this talk, we will discuss 1) why strain-level inference is important for understanding the function, ecology, and evolution of the gut microbiome, 2) the challenges associated with inferring strain-level microbiome composition from metagenomic sequencing data, 3) current methods for inferring strain-level microbiome composition, and 4) open problems for the field.

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