Supervised Machine Learning Enables Geospatial Microbial Provenance

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applica...

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Bibliographic Details
Published in:	Genes Vol. 13; no. 10; p. 1914
Main Authors:	Bhattacharya, Chandrima, Tierney, Braden T, Ryon, Krista A, Bhattacharyya, Malay, Hastings, Jaden J A, Basu, Srijani, Bhattacharya, Bodhisatwa, Bagchi, Debneel, Mukherjee, Somsubhro, Wang, Lu, Henaff, Elizabeth M, Mason, Christopher E
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 21-10-2022 MDPI
Subjects:	Algorithms Bioindicators Cities Classification Consortia Datasets Discriminant analysis Environmental monitoring Fingerprinting Forensic science Forensic sciences Gene expression Genomes Genomics Indicator species Learning algorithms Machine learning Metagenome Metagenomics Metagenomics - methods Methods Microbial forensics Microbiomes Microbiota - genetics Oceans Provenance Public health Sparsity Supervised Machine Learning Support vector machines Taxonomy Urban environments United States microbial fingerprint supervised machine learning bioindicator species microbial forensics metagenomics
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Summary:	The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset-the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2073-4425 2073-4425
DOI:	10.3390/genes13101914