Scalable Microbial Strain Inference in Metagenomic Data Using StrainFacts.

ABSTRACT: While genome databases are nearing a complete catalog of species commonly inhabiting the human gut, their representation of intraspecific diversity is lacking for all but the most abundant and frequently studied taxa. Statistical deconvolution of allele frequencies from shotgun metagenomic data into strain genotypes and relative abundances is a promising approach, but existing methods are limited by computational scalability. Here we introduce StrainFacts, a method for strain deconvolution that enables inference across tens of thousands of metagenomes. We harness a "fuzzy" genotype approximation that makes the underlying graphical model fully differentiable, unlike existing methods. This allows parameter estimates to be optimized with gradient-based methods, speeding up model fitting by tw

SUBMITTER: Smith BJ 

PROVIDER: S-EPMC9580935 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature