Predicting hydrocarbon presence in marine cold seep sediments using machine learning models trained with benthic bacterial 16S rRNA taxonomy

Our study showcases an important use of bioinformatics in an interdisciplinary context, by combining hydrocarbon geochemistry and microbial biodiversity DNA sequencing profiles. We trained and compared different machine learning models on 16S rRNA-based bacterial taxonomy data using 377 DNA sequencing libraries from marine surface sediments in two different hydrocarbon prospective marine basins from different parts of the global ocean to predict the hydrocarbon status of sediment samples. Of all algorithms tested, Gradient Boosting Machines worked best for this objective. Feature importance scores from the models highlighted that in Gulf of Mexico samples, members of the <i>Aminicenantales</i> order and <i>Campylobacterota</i> lineages were most diagnostic for the presence of low molecular weight hydrocarbon gases. The <i>Campylobacterota</i> lineage was also important in NW Atlantic Scotian Slope sediments, along with sequences affiliated with the class-level group JS1 (within the <i>Caldatribacteriota</i> phylum) for determining hydrocarbon-positive sites, though several features appeared to be basin-specific. Importantly, models had a high prediction accuracy when predicting samples from the same basin but were less effective in predicting the hydrocarbon status in reciprocal basin testing, pointing to the ecological differences in hydrocarbon-driven environmental selection in different parts of the ocean. However, combined models using a refined set of predictive features improved cross-basin performance, highlighting the feasibility of a broader application. Results highlight the exciting potential of microbial taxonomy-based machine learning models in predicting broader ecological, oceanographic, and geological phenomena at the biosphere-geosphere interface.