Data_Sheet_1_Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance.PDF
Oxygen constitutes one of the strongest factors explaining microbial taxonomic variability in deep-sea sediments. However, deep-sea microbiome studies often lack the spatial resolution to study the oxygen gradient and transition zone beyond the oxic-anoxic dichotomy, thus leaving important questions...
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ftfrontimediafig:oai:figshare.com:article/19784689 2023-05-15T14:59:53+02:00 Data_Sheet_1_Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance.PDF Tor Einar Møller Sven Le Moine Bauer Bjarte Hannisdal Rui Zhao Tamara Baumberger Desiree L. Roerdink Amandine Dupuis Ingunn H. Thorseth Rolf Birger Pedersen Steffen Leth Jørgensen 2022-05-18T04:54:42Z https://doi.org/10.3389/fmicb.2022.804575.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Mapping_Microbial_Abundance_and_Prevalence_to_Changing_Oxygen_Concentration_in_Deep-Sea_Sediments_Using_Machine_Learning_and_Differential_Abundance_PDF/19784689 unknown doi:10.3389/fmicb.2022.804575.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Mapping_Microbial_Abundance_and_Prevalence_to_Changing_Oxygen_Concentration_in_Deep-Sea_Sediments_Using_Machine_Learning_and_Differential_Abundance_PDF/19784689 CC BY 4.0 CC-BY Microbiology Microbial Genetics Microbial Ecology Mycology Support Vector Machines Compositional Data Analysis Arctic Mid-Ocean Ridge Norwegian-Greenland Sea threshold response classification Dataset 2022 ftfrontimediafig https://doi.org/10.3389/fmicb.2022.804575.s001 2022-05-18T23:07:58Z Oxygen constitutes one of the strongest factors explaining microbial taxonomic variability in deep-sea sediments. However, deep-sea microbiome studies often lack the spatial resolution to study the oxygen gradient and transition zone beyond the oxic-anoxic dichotomy, thus leaving important questions regarding the microbial response to changing conditions unanswered. Here, we use machine learning and differential abundance analysis on 184 samples from 11 sediment cores retrieved along the Arctic Mid-Ocean Ridge to study how changing oxygen concentrations (1) are predicted by the relative abundance of higher taxa and (2) influence the distribution of individual Operational Taxonomic Units. We find that some of the most abundant classes of microorganisms can be used to classify samples according to oxygen concentration. At the level of Operational Taxonomic Units, however, representatives of common classes are not differentially abundant from high-oxic to low-oxic conditions. This weakened response to changing oxygen concentration suggests that the abundance and prevalence of highly abundant OTUs may be better explained by other variables than oxygen. Our results suggest that a relatively homogeneous microbiome is recruited to the benthos, and that the microbiome then becomes more heterogeneous as oxygen drops below 25 μM. Our analytical approach takes into account the oft-ignored compositional nature of relative abundance data, and provides a framework for extracting biologically meaningful associations from datasets spanning multiple sedimentary cores. Dataset Arctic Greenland Greenland Sea Frontiers: Figshare Arctic Greenland |
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Open Polar |
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Frontiers: Figshare |
op_collection_id |
ftfrontimediafig |
language |
unknown |
topic |
Microbiology Microbial Genetics Microbial Ecology Mycology Support Vector Machines Compositional Data Analysis Arctic Mid-Ocean Ridge Norwegian-Greenland Sea threshold response classification |
spellingShingle |
Microbiology Microbial Genetics Microbial Ecology Mycology Support Vector Machines Compositional Data Analysis Arctic Mid-Ocean Ridge Norwegian-Greenland Sea threshold response classification Tor Einar Møller Sven Le Moine Bauer Bjarte Hannisdal Rui Zhao Tamara Baumberger Desiree L. Roerdink Amandine Dupuis Ingunn H. Thorseth Rolf Birger Pedersen Steffen Leth Jørgensen Data_Sheet_1_Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance.PDF |
topic_facet |
Microbiology Microbial Genetics Microbial Ecology Mycology Support Vector Machines Compositional Data Analysis Arctic Mid-Ocean Ridge Norwegian-Greenland Sea threshold response classification |
description |
Oxygen constitutes one of the strongest factors explaining microbial taxonomic variability in deep-sea sediments. However, deep-sea microbiome studies often lack the spatial resolution to study the oxygen gradient and transition zone beyond the oxic-anoxic dichotomy, thus leaving important questions regarding the microbial response to changing conditions unanswered. Here, we use machine learning and differential abundance analysis on 184 samples from 11 sediment cores retrieved along the Arctic Mid-Ocean Ridge to study how changing oxygen concentrations (1) are predicted by the relative abundance of higher taxa and (2) influence the distribution of individual Operational Taxonomic Units. We find that some of the most abundant classes of microorganisms can be used to classify samples according to oxygen concentration. At the level of Operational Taxonomic Units, however, representatives of common classes are not differentially abundant from high-oxic to low-oxic conditions. This weakened response to changing oxygen concentration suggests that the abundance and prevalence of highly abundant OTUs may be better explained by other variables than oxygen. Our results suggest that a relatively homogeneous microbiome is recruited to the benthos, and that the microbiome then becomes more heterogeneous as oxygen drops below 25 μM. Our analytical approach takes into account the oft-ignored compositional nature of relative abundance data, and provides a framework for extracting biologically meaningful associations from datasets spanning multiple sedimentary cores. |
format |
Dataset |
author |
Tor Einar Møller Sven Le Moine Bauer Bjarte Hannisdal Rui Zhao Tamara Baumberger Desiree L. Roerdink Amandine Dupuis Ingunn H. Thorseth Rolf Birger Pedersen Steffen Leth Jørgensen |
author_facet |
Tor Einar Møller Sven Le Moine Bauer Bjarte Hannisdal Rui Zhao Tamara Baumberger Desiree L. Roerdink Amandine Dupuis Ingunn H. Thorseth Rolf Birger Pedersen Steffen Leth Jørgensen |
author_sort |
Tor Einar Møller |
title |
Data_Sheet_1_Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance.PDF |
title_short |
Data_Sheet_1_Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance.PDF |
title_full |
Data_Sheet_1_Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance.PDF |
title_fullStr |
Data_Sheet_1_Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance.PDF |
title_full_unstemmed |
Data_Sheet_1_Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance.PDF |
title_sort |
data_sheet_1_mapping microbial abundance and prevalence to changing oxygen concentration in deep-sea sediments using machine learning and differential abundance.pdf |
publishDate |
2022 |
url |
https://doi.org/10.3389/fmicb.2022.804575.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Mapping_Microbial_Abundance_and_Prevalence_to_Changing_Oxygen_Concentration_in_Deep-Sea_Sediments_Using_Machine_Learning_and_Differential_Abundance_PDF/19784689 |
geographic |
Arctic Greenland |
geographic_facet |
Arctic Greenland |
genre |
Arctic Greenland Greenland Sea |
genre_facet |
Arctic Greenland Greenland Sea |
op_relation |
doi:10.3389/fmicb.2022.804575.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Mapping_Microbial_Abundance_and_Prevalence_to_Changing_Oxygen_Concentration_in_Deep-Sea_Sediments_Using_Machine_Learning_and_Differential_Abundance_PDF/19784689 |
op_rights |
CC BY 4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3389/fmicb.2022.804575.s001 |
_version_ |
1766331996623077376 |