Novel Insights into the Ecology of the Microbial Rare Biosphere in the Arctic Ocean using Microbiome DNA Sequencing and Machine Learning

The Arctic Ocean is highly impacted by climate change, yet its effects on prokaryotic communities, particularly the rare biosphere, remain largely unexplored. The rare biosphere plays a crucial role in ecosystem resistance and resilience, but is often overlooked in Arctic microbial studies.
In this study, we address multiple methodological challenges in the analysis of microbiome data from the Arctic Ocean, which contributed for the creation of a multi-year dataset of the Arctic Ocean (2016-2020). Additionally, we improved computational methods to define and study the rare biosphere, with the use of unsupervised machine learning. Finally, we combined unsupervised and rule-based machine learning to reveal complex and non-linear relationships that were missed by traditional statistical methods.

Francisco Pascoal (CIIMAR)

Francisco Pascoal, MSc in Microbiology and PhD candidate at CIIMAR, specializes in environmental microbiome data analysis. His research integrates amplicon sequencing, metagenomics, and data science to address challenges in microbiome studies. His PhD focuses on the rare biosphere’s response to climate change in the Arctic Ocean, leveraging machine learning to uncover ecological patterns. By advancing computational strategies, he aims to enhance the interpretation of microbial diversity assessments in marine ecosystems.

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