Speaker
Description
Authors: Xiaoyu Shan, Otto X. Cordero
Recent studies have shown that microbiomes are composed of groups of functionally cohesive taxa, whose abundance is more stable and better associated with metabolic fluxes than that of any individual taxon. However, identifying these functional groups in a manner that is independent from error-prone functional gene annotations remains a major open problem. Here, we develop a novel approach that identifies groups of taxa that when combined result in strong and stable statistical associations with ecological variables, despite weak association at the individual taxon level. We leverage this approach on three distinct data sets: i) on data of replicate microcosm, our unsupervised algorithm reproduces experimentally validated functional groups that divide carbon utilizing communities into glycolytic and gluconeogenic groups; ii) when leveraged against the oceans microbiome data, our approach discover groups of aerobic and anaerobic ammonia oxidizers which collectively are strongly correlated with the abundance of nitrate in the water column, and finally, iii) we show that our framework enables detection of minimal assemblages strongly predictive of metabolites level in animal gut microbiomes. This work advances our understanding of structure-function relationship in complex microbiomes and provided a powerful computational tool to discover functional groups across different ecosystems.
