Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline
One of the major methods to identify microbial community composition, to unravel microbial population dynamics, and to explore microbial diversity in environmental samples is high-throughput DNA- or RNA-based 16S rRNA (gene) amplicon sequencing in combination with bioinformatics analyses. However, f...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-10-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2020.550420/full |
| _version_ | 1818242200406851584 |
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| author | Daniel Straub Daniel Straub Nia Blackwell Adrian Langarica-Fuentes Alexander Peltzer Sven Nahnsen Sara Kleindienst |
| author_facet | Daniel Straub Daniel Straub Nia Blackwell Adrian Langarica-Fuentes Alexander Peltzer Sven Nahnsen Sara Kleindienst |
| author_sort | Daniel Straub |
| collection | DOAJ |
| description | One of the major methods to identify microbial community composition, to unravel microbial population dynamics, and to explore microbial diversity in environmental samples is high-throughput DNA- or RNA-based 16S rRNA (gene) amplicon sequencing in combination with bioinformatics analyses. However, focusing on environmental samples from contrasting habitats, it was not systematically evaluated (i) which analysis methods provide results that reflect reality most accurately, (ii) how the interpretations of microbial community studies are biased by different analysis methods and (iii) if the most optimal analysis workflow can be implemented in an easy-to-use pipeline. Here, we compared the performance of 16S rRNA (gene) amplicon sequencing analysis tools (i.e., Mothur, QIIME1, QIIME2, and MEGAN) using three mock datasets with known microbial community composition that differed in sequencing quality, species number and abundance distribution (i.e., even or uneven), and phylogenetic diversity (i.e., closely related or well-separated amplicon sequences). Our results showed that QIIME2 outcompeted all other investigated tools in sequence recovery (>10 times fewer false positives), taxonomic assignments (>22% better F-score) and diversity estimates (>5% better assessment), suggesting that this approach is able to reflect the in situ microbial community most accurately. Further analysis of 24 environmental datasets obtained from four contrasting terrestrial and freshwater sites revealed dramatic differences in the resulting microbial community composition for all pipelines at genus level. For instance, at the investigated river water sites Sphaerotilus was only reported when using QIIME1 (8% abundance) and Agitococcus with QIIME1 or QIIME2 (2 or 3% abundance, respectively), but both genera remained undetected when analyzed with Mothur or MEGAN. Since these abundant taxa probably have implications for important biogeochemical cycles (e.g., nitrate and sulfate reduction) at these sites, their detection and semi-quantitative enumeration is crucial for valid interpretations. A high-performance computing conformant workflow was constructed to allow FAIR (Findable, Accessible, Interoperable, and Re-usable) 16S rRNA (gene) amplicon sequence analysis starting from raw sequence files, using the most optimal methods identified in our study. Our presented workflow should be considered for future studies, thereby facilitating the analysis of high-throughput 16S rRNA (gene) sequencing data substantially, while maximizing reliability and confidence in microbial community data analysis. |
| first_indexed | 2024-12-12T13:41:27Z |
| format | Article |
| id | doaj.art-803564f752704ae9ab8da9948947d007 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-12-12T13:41:27Z |
| publishDate | 2020-10-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Microbiology |
| spelling | doaj.art-803564f752704ae9ab8da9948947d0072022-12-22T00:22:48ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2020-10-011110.3389/fmicb.2020.550420550420Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing PipelineDaniel Straub0Daniel Straub1Nia Blackwell2Adrian Langarica-Fuentes3Alexander Peltzer4Sven Nahnsen5Sara Kleindienst6Microbial Ecology, Center for Applied Geoscience, Department of Geosciences, University of Tübingen, Tübingen, GermanyQuantitative Biology Center (QBiC), University of Tübingen, Tübingen, GermanyMicrobial Ecology, Center for Applied Geoscience, Department of Geosciences, University of Tübingen, Tübingen, GermanyMicrobial Ecology, Center for Applied Geoscience, Department of Geosciences, University of Tübingen, Tübingen, GermanyQuantitative Biology Center (QBiC), University of Tübingen, Tübingen, GermanyQuantitative Biology Center (QBiC), University of Tübingen, Tübingen, GermanyMicrobial Ecology, Center for Applied Geoscience, Department of Geosciences, University of Tübingen, Tübingen, GermanyOne of the major methods to identify microbial community composition, to unravel microbial population dynamics, and to explore microbial diversity in environmental samples is high-throughput DNA- or RNA-based 16S rRNA (gene) amplicon sequencing in combination with bioinformatics analyses. However, focusing on environmental samples from contrasting habitats, it was not systematically evaluated (i) which analysis methods provide results that reflect reality most accurately, (ii) how the interpretations of microbial community studies are biased by different analysis methods and (iii) if the most optimal analysis workflow can be implemented in an easy-to-use pipeline. Here, we compared the performance of 16S rRNA (gene) amplicon sequencing analysis tools (i.e., Mothur, QIIME1, QIIME2, and MEGAN) using three mock datasets with known microbial community composition that differed in sequencing quality, species number and abundance distribution (i.e., even or uneven), and phylogenetic diversity (i.e., closely related or well-separated amplicon sequences). Our results showed that QIIME2 outcompeted all other investigated tools in sequence recovery (>10 times fewer false positives), taxonomic assignments (>22% better F-score) and diversity estimates (>5% better assessment), suggesting that this approach is able to reflect the in situ microbial community most accurately. Further analysis of 24 environmental datasets obtained from four contrasting terrestrial and freshwater sites revealed dramatic differences in the resulting microbial community composition for all pipelines at genus level. For instance, at the investigated river water sites Sphaerotilus was only reported when using QIIME1 (8% abundance) and Agitococcus with QIIME1 or QIIME2 (2 or 3% abundance, respectively), but both genera remained undetected when analyzed with Mothur or MEGAN. Since these abundant taxa probably have implications for important biogeochemical cycles (e.g., nitrate and sulfate reduction) at these sites, their detection and semi-quantitative enumeration is crucial for valid interpretations. A high-performance computing conformant workflow was constructed to allow FAIR (Findable, Accessible, Interoperable, and Re-usable) 16S rRNA (gene) amplicon sequence analysis starting from raw sequence files, using the most optimal methods identified in our study. Our presented workflow should be considered for future studies, thereby facilitating the analysis of high-throughput 16S rRNA (gene) sequencing data substantially, while maximizing reliability and confidence in microbial community data analysis.https://www.frontiersin.org/articles/10.3389/fmicb.2020.550420/full16S rRNAamplicon sequencingenvironmental samplesbioinformaticsnf-core/ampliseq |
| spellingShingle | Daniel Straub Daniel Straub Nia Blackwell Adrian Langarica-Fuentes Alexander Peltzer Sven Nahnsen Sara Kleindienst Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline Frontiers in Microbiology 16S rRNA amplicon sequencing environmental samples bioinformatics nf-core/ampliseq |
| title | Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline |
| title_full | Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline |
| title_fullStr | Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline |
| title_full_unstemmed | Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline |
| title_short | Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline |
| title_sort | interpretations of environmental microbial community studies are biased by the selected 16s rrna gene amplicon sequencing pipeline |
| topic | 16S rRNA amplicon sequencing environmental samples bioinformatics nf-core/ampliseq |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2020.550420/full |
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