DGCNN approach links metagenome-derived taxon and functional information providing insight into global soil organic carbon.

ABSTRACT: Metagenomics can provide insight into the microbial taxa present in a sample and, through gene identification, the functional potential of the community. However, taxonomic and functional information are typically considered separately in downstream analyses. We develop interpretable machine learning (ML) approaches for modelling metagenomic data, combining the biological representation of species with their associated genetically encoded functions within models. We apply our methods to investigate soil organic carbon (SOC) stocks. First, we combine a diverse global set of soil microbiome samples with environmental data, improving the predictive performance of classic ML and providing new insights into the role of soil microbiomes in global carbon cycling. Our network analysis of predictiv

SUBMITTER: Gardiner LJ 

PROVIDER: S-EPMC11513995 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature