Supervised Machine Learning Enables Geospatial Microbial Provenance

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Persistence of location-specific microbial signatures on boats during voyages

Objects collect microorganisms from their surroundings and develop a microbial "fingerprint" that may be useful for determining an object's past location (provenance). It may be possible to use ubiquitous microorganisms for forensics or as environmental sensors. Here, we use microbial communities in the Chesapeake Bay region to demonstrate the use of natural microorganisms as biological sensors to determine the past location of boats. The microbiomes of two boats and of the open water were sampled as these vessels traveled from the Port of Baltimore to the Port of Norfolk, and back to Baltimore. 16S rRNA sequencing was performed to identify microorganisms. Differential abundance and machine learning analyses were utilized to identify microbial signatures and predicted probabilities which were used to determine the vessel's previous location. The work presented here provides a better understanding of how microbes in aquatic systems can be leveraged as utility for object biosensors.