Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments

Hempel, Christopher; Wright, Natalie Andrews; Harvie, Julia; Hleap, Jose Sergio; Adamowicz, Sarah J.; Steinke, Dirk

doi:10.1093/nar/gkac689

Cited by 27 publications

(22 citation statements)

References 86 publications

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“…However, many commercial kits are optimized to remove human, mouse, rat and bacteria rRNA, and may not deplete rRNA from all species; M: Perform poly(A) enrichment to retain only eukaryotic mRNA. This will eliminate prokaryotic RNA, ribosomal and small RNAs, which make up the majority of total RNA B: If rRNA persists, use it to perform taxonomic identification analysis [97][98][99][100] Low eRNA recovery eRNA samples are composed of extraorganismal RNA from macro-organisms and whole microorganism. Thus, eRNA reads will be in the minority (estimated <1%), resulting in low power of bioinformatic analyses to detect eRNA based changes in gene expression E: Pre-filter water with fine mesh to size-selectively remove organisms; E: Use large filter pore size to reduce capture of microorganisms (i.e.…”

Section: Figures and Tablesmentioning

confidence: 99%

Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms?

Hechler

Yates

Chain

et al. 2022

Preprint

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To safeguard biodiversity in a changing climate, we require taxonomic information about species turnover and insights into the health of organisms. Environmental DNA approaches are increasingly used for species identification, but cannot provide functional insights. Transcriptomic methods reveal the physiological states of macroorganisms, but are currently species specific and require tissue sampling or animal sacrifice, making community-wide assessments challenging. Here, we test if broad functional information (activity level of the transcribed genes) can be harnessed from environmental RNA (eRNA), which includes extra-organismal RNA from macroorganisms along with whole microorganisms. We exposed Daphnia pulex as well as phytoplankton prey and microorganism colonizers to control (20 °C) and heat stress (28 °C) conditions for seven days. We sequenced eRNA from tank water (after complete removal of Daphnia) as well as RNA from Daphnia tissue, enabling comparisons of extra-organismal and organismal RNA based gene expression profiles. Both RNA types detected similar gene expression responses. Using eRNA, we identified 42 Daphnia genes to be differentially expressed following heat stress. Of these, 62% were also differentially expressed and exhibited similar levels of relative expression in organismal RNA. Both RNA types recovered similar Daphnia heat stress responses via gene ontology terms. In addition to the extra-organismal Daphnia response, eRNA detected community-wide heat stress responses consisting of 199 differentially expressed genes across 9 taxa, and distinct functional profiles via KEGG orthologs. Our study demonstrates that environmental transcriptomics based on eRNA can non-invasively reveal gene expression responses of macroorganisms following environmental changes, with broad potential implications for the biomonitoring of ecological health across the trophic chain.

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Section: Figures and Tablesmentioning

confidence: 99%

Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms?

Hechler

Yates

Chain

et al. 2022

Preprint

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“…The data used in this study originate from an earlier study, in which we applied total RNA-Seq and metagenomics and investigated 672 combinations of data-processing tools to identify the best-performing sequencing method and combination of tools to characterize taxonomically a mock microbial community (Hempel et al, 2022). Details of the laboratory and bioinformatics processing steps can be found there (Hempel et al, 2022). In summary, we co-extracted DNA and RNA in parallel using a modified version of the Quick-DNA/RNA Microprep Plus Kit (Zymo Research; Irvine; CA U.S.A.).…”

Section: Methodsmentioning

confidence: 99%

“…A few studies compared the performance of total RNA-Seq and metabarcoding (Lanzén et al, 2011; Yan et al, 2018) or metagenomics (Hempel et al, 2022; Lanzén et al, 2011; Shi, Tyson, Eppley, & Delong, 2011; Urich et al, 2014; Uyaguari-Diaz et al, 2016) for the analysis of microbial community composition. However, the use of total RNA-Seq to analyze microbial communities remains rare, and a comparison of total RNA-Seq and metagenomics in terms of SSU rRNA reconstruction is lacking, although the results of such a comparison could impact our ability to categorize global biodiversity and analyze microbial communities.…”

Section: Introductionmentioning

confidence: 99%

“…In an earlier study, we were able to show that total RNA-Seq characterized a mock microbial community consisting of 10 species more accurately than metagenomics at almost one order of magnitude lower sequencing depth (Hempel et al, 2022). For the present study, we use the same data to test the performance of metagenomics and total RNA-Seq in terms of SSU rRNA completeness, i.e., the portion of the SSU rRNA that can be successfully reconstructed.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of Small Subunit Ribosomal RNA from High-Throughput Sequencing Data: A Comparative Study of Metagenomics and Total RNA Sequencing

Hempel

Carson

Elliott

et al. 2022

Preprint

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The small subunit (SSU) ribosomal RNA (rRNA) is the most commonly used marker for the identification of microbial taxa, but its full-length reconstruction from high-throughput sequencing (HTS) data remains challenging, especially for complex and diverse environmental samples. Metagenomics and total RNA sequencing (total RNA-Seq) are target-PCR-free HTS methods that are used to characterize microbial communities and simultaneously reconstruct SSU rRNA sequences. However, more testing is required to determine and improve their effectiveness. In this study, we processed metagenomics and total RNA-Seq data retrieved from a commercially available mock microbial community using 112 combinations of commonly used data-processing tools, determined SSU rRNA reconstruction completeness of both sequencing methods for each species in the mock community, and analyzed the impact of data-processing tools on SSU rRNA and genome completeness. Total RNA-Seq allowed for the complete or near-complete reconstruction of all mock community SSU rRNA sequences and outperformed metagenomics. SSU rRNA completeness of metagenomics strongly correlated with the genome size of mock community species. The impact of data-processing tools was overall low, although certain tools resulted in significantly lower SSU rRNA completeness. These results are promising for the high-throughput reconstruction of novel full-length SSU rRNA sequences and could advance the simultaneous application of multiple -omics approaches in routine environmental assessments to allow for more holistic assessments of ecosystems.

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“…Cellular RNA consists mostly of rRNA, including 16S and 18S rRNA, which means that a large portion of total RNA-Seq data can be used for taxonomic annotations of microbes. In a previous study, we showed that total RNA-Seq can identify a microbial mock community consisting of 10 species more accurately than metagenomics at almost one order of magnitude lower sequencing depth [31]. Therefore, total RNA-Seq combines the advantages of both amplicon sequencing and metagenomics, as it avoids targeted PCR while producing large amounts of 16S and 18S sequences that can be taxonomically annotated.…”

Section: Introductionmentioning

confidence: 99%

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

Hempel

Buchner

Mack

et al. 2022

Preprint

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Background: Microbes 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. In 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. Results: Sequencing and data-processing methods had a substantial impact on stressor prediction. While omics-based methods detected much more 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. Conclusion: 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.

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Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments

Cited by 27 publications

References 86 publications

Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms?

Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms?

Reconstruction of Small Subunit Ribosomal RNA from High-Throughput Sequencing Data: A Comparative Study of Metagenomics and Total RNA Sequencing

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

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