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

Hydrocarbon seepage in marine sediments exerts selective pressure on benthic microbiomes. Accordingly, microbial community composition in these sediments can reflect the presence of hydrocarbons, with specific groups being more prolific in association with seepage. Here, we tested machine learning models with large 16S rRNA gene amplicon data sets derived from marine sediments in deep-sea hydrocarbon prospective areas of the Eastern Gulf of Mexico and NW Atlantic Scotian Slope. Utilizing H2O's AutoML machine learning platform, it was determined that Gradient Boosting Machines performed best for creating 16S rRNA-based models that successfully predict the presence of hydrocarbons. Feature importance scores from the models revealed that in Gulf of Mexico samples, members of the IMPORTANCE: 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