Bakdrive: identifying a minimum set of bacterial species driving interactions across multiple microbial communities

MOTIVATION: Interactions among microbes within microbial communities have been shown to play crucial roles in human health. In spite of recent progress, low-level knowledge of bacteria driving microbial interactions within microbiomes remains unknown, limiting our ability to fully decipher and control microbial communities. RESULTS: We present a novel approach for identifying species driving interactions within microbiomes. Bakdrive infers ecological networks of given metagenomic sequencing samples and identifies minimum sets of driver species (MDS) using control theory. Bakdrive has three key innovations in this space: (i) it leverages inherent information from metagenomic sequencing samples to identify driver species, (ii) it explicitly takes host-specific variation into consideration, and (iii) it does not require a known ecological network. In extensive simulated data, we demonstrate identifying driver species identified from healthy donor samples and introducing them to the disease samples, we can restore the gut microbiome in recurrent Clostridioides difficile (rCDI) infection patients to a healthy state. We also applied Bakdrive to two real datasets, rCDI and Crohn's disease patients, uncovering driver species consistent with previous work. Bakdrive represents a novel approach for capturing microbial interactions. AVAILABILITY AND IMPLEMENTATION: Bakdrive is open-source and available at: https://gitlab.com/treangenlab/bakdrive.