Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data
IntroductionMicrobes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learnin...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-11-01
|
| Series: | Frontiers in Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1217750/full |
| _version_ | 1797465561255378944 |
|---|---|
| author | Christopher A. Hempel Christopher A. Hempel Dominik Buchner Leoni Mack Marie V. Brasseur Dan Tulpan Dan Tulpan Florian Leese Florian Leese Dirk Steinke Dirk Steinke |
| author_facet | Christopher A. Hempel Christopher A. Hempel Dominik Buchner Leoni Mack Marie V. Brasseur Dan Tulpan Dan Tulpan Florian Leese Florian Leese Dirk Steinke Dirk Steinke |
| author_sort | Christopher A. Hempel |
| collection | DOAJ |
| description | IntroductionMicrobes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amplicon sequencing is typically applied to assess microbial communities, metagenomics and total RNA sequencing (herein summarized as omics-based methods) can provide a more holistic picture of microbial biodiversity at sufficient sequencing depths. Despite this advantage, amplicon sequencing and omics-based methods have not yet been compared for taxonomy-based environmental assessments with machine learning.MethodsIn this study, we applied 16S and ITS-2 sequencing, metagenomics, and total RNA sequencing to samples from a stream mesocosm experiment that investigated the impacts of two aquatic stressors, insecticide and increased fine sediment deposition, on stream biodiversity. We processed the data using similarity clustering and denoising (only applicable to amplicon sequencing) as well as multiple taxonomic levels, data types, feature selection, and machine learning algorithms and evaluated the stressor prediction performance of each generated model for a total of 1,536 evaluated combinations of taxonomic datasets and data-processing methods.ResultsSequencing and data-processing methods had a substantial impact on stressor prediction. While omics-based methods detected a higher diversity of taxa than amplicon sequencing, 16S sequencing outperformed all other sequencing methods in terms of stressor prediction based on the Matthews Correlation Coefficient. However, even the highest observed performance for 16S sequencing was still only moderate. Omics-based methods performed poorly overall, but this was likely due to insufficient sequencing depth. Data types had no impact on performance while feature selection significantly improved performance for omics-based methods but not for amplicon sequencing.DiscussionWe conclude that amplicon sequencing might be a better candidate for machine-learning-based environmental stressor prediction than omics-based methods, but the latter require further research at higher sequencing depths to confirm this conclusion. More sampling could improve stressor prediction performance, and while this was not possible in the context of our study, thousands of sampling sites are monitored for routine environmental assessments, providing an ideal framework to further refine the approach for possible implementation in environmental diagnostics. |
| first_indexed | 2024-03-09T18:23:12Z |
| format | Article |
| id | doaj.art-2c29af2200d54461afcd1aa297c11cd0 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-03-09T18:23:12Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-2c29af2200d54461afcd1aa297c11cd02023-11-24T08:04:42ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2023-11-011410.3389/fmicb.2023.12177501217750Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned dataChristopher A. Hempel0Christopher A. Hempel1Dominik Buchner2Leoni Mack3Marie V. Brasseur4Dan Tulpan5Dan Tulpan6Florian Leese7Florian Leese8Dirk Steinke9Dirk Steinke10Department of Integrative Biology, University of Guelph, Guelph, ON, CanadaCentre for Biodiversity Genomics, University of Guelph, Guelph, ON, CanadaAquatic Ecosystem Research, University of Duisburg-Essen, Essen, GermanyFaculty of Aquatic Ecology, University of Duisburg-Essen, Essen, GermanyLeibniz Institute for the Analysis of Biodiversity Change, Zoological Research Museum A. Koenig, Bonn, GermanySchool of Computer Science, University of Guelph, Guelph, ON, CanadaDepartment of Animal Biosciences, University of Guelph, Guelph, ON, CanadaAquatic Ecosystem Research, University of Duisburg-Essen, Essen, GermanyCentre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Essen, GermanyDepartment of Integrative Biology, University of Guelph, Guelph, ON, CanadaCentre for Biodiversity Genomics, University of Guelph, Guelph, ON, CanadaIntroductionMicrobes are increasingly (re)considered for environmental assessments because they are powerful indicators for the health of ecosystems. The complexity of microbial communities necessitates powerful novel tools to derive conclusions for environmental decision-makers, and machine learning is a promising option in that context. While amplicon sequencing is typically applied to assess microbial communities, metagenomics and total RNA sequencing (herein summarized as omics-based methods) can provide a more holistic picture of microbial biodiversity at sufficient sequencing depths. Despite this advantage, amplicon sequencing and omics-based methods have not yet been compared for taxonomy-based environmental assessments with machine learning.MethodsIn this study, we applied 16S and ITS-2 sequencing, metagenomics, and total RNA sequencing to samples from a stream mesocosm experiment that investigated the impacts of two aquatic stressors, insecticide and increased fine sediment deposition, on stream biodiversity. We processed the data using similarity clustering and denoising (only applicable to amplicon sequencing) as well as multiple taxonomic levels, data types, feature selection, and machine learning algorithms and evaluated the stressor prediction performance of each generated model for a total of 1,536 evaluated combinations of taxonomic datasets and data-processing methods.ResultsSequencing and data-processing methods had a substantial impact on stressor prediction. While omics-based methods detected a higher diversity of taxa than amplicon sequencing, 16S sequencing outperformed all other sequencing methods in terms of stressor prediction based on the Matthews Correlation Coefficient. However, even the highest observed performance for 16S sequencing was still only moderate. Omics-based methods performed poorly overall, but this was likely due to insufficient sequencing depth. Data types had no impact on performance while feature selection significantly improved performance for omics-based methods but not for amplicon sequencing.DiscussionWe conclude that amplicon sequencing might be a better candidate for machine-learning-based environmental stressor prediction than omics-based methods, but the latter require further research at higher sequencing depths to confirm this conclusion. More sampling could improve stressor prediction performance, and while this was not possible in the context of our study, thousands of sampling sites are monitored for routine environmental assessments, providing an ideal framework to further refine the approach for possible implementation in environmental diagnostics.https://www.frontiersin.org/articles/10.3389/fmicb.2023.1217750/fullmetabarcodingmetatranscriptomicsfreshwaterstressor predictionbioinformaticsExStream |
| spellingShingle | Christopher A. Hempel Christopher A. Hempel Dominik Buchner Leoni Mack Marie V. Brasseur Dan Tulpan Dan Tulpan Florian Leese Florian Leese Dirk Steinke Dirk Steinke Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data Frontiers in Microbiology metabarcoding metatranscriptomics freshwater stressor prediction bioinformatics ExStream |
| title | Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data |
| title_full | Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data |
| title_fullStr | Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data |
| title_full_unstemmed | Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data |
| title_short | Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data |
| title_sort | predicting environmental stressor levels with machine learning a comparison between amplicon sequencing metagenomics and total rna sequencing based on taxonomically assigned data |
| topic | metabarcoding metatranscriptomics freshwater stressor prediction bioinformatics ExStream |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1217750/full |
| work_keys_str_mv | AT christopherahempel predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT christopherahempel predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT dominikbuchner predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT leonimack predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT marievbrasseur predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT dantulpan predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT dantulpan predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT florianleese predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT florianleese predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT dirksteinke predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata AT dirksteinke predictingenvironmentalstressorlevelswithmachinelearningacomparisonbetweenampliconsequencingmetagenomicsandtotalrnasequencingbasedontaxonomicallyassigneddata |