The ability to detect and quantify microbiota over time from shotgun metagenomic data has a plethora of clinical, basic science, and public health applications. Given these applications, and the observation that pathogens and other taxa of interest can reside at low relative abundance, there is a critical need for algorithms that accurately profile low-abundance microbial taxa with strain-level resolution. In this work, we present ChronoStrain, a sequence quality- and time-aware Bayesian model for profiling strains in longitudinal samples. ChronoStrain explicitly models the presence or absence of each strain and produces a probability distribution over abundance trajectories for each strain. In addition to synthetic benchmarks that benchmark its performance, we analyze two human microbiome datasets to:(1) demonstrate its interpretability for profiling E. coli strains in recurring urinary tract infections, and (2) showcase its accuracy for detecting low-abundance E. faecalis strains in infant fecal samples. Full publication: Kim, Y. et al, Longitudinal profiling of low-abundance strains in microbiomes with ChronoStrain. Nature Microbiology, 2025 (in press).