Comparative Analysis of Metagenomic (Amplicon and Shotgun) DNA Sequencing to Characterize Microbial Communities in Household On-Site Wastewater Treatment Systems
Bibliographic record
Abstract
The performance of on-site wastewater treatment systems (OWTSs) can be improved by altering digester design and by manipulating environmental variables that affect microbial community composition. Community composition can potentially be assessed using high-throughput DNA sequencing, but the two most common methods of community DNA sequencing (16S and shotgun sequencing) generally yield different taxonomic identification profiles and can perform differently according to the sampled environment. To evaluate the use of these two approaches in monitoring OWTS operation, we conducted a comparative parallel analysis using both 16S rDNA and shotgun sequencing in a controlled field study. Results indicate that when examining microorganisms above 0.1% relative abundance, 16S and shotgun sequencing produced similar results in terms of individual sample species richness and between-sample community similarity. However, shotgun sequencing provided comparatively higher taxonomic richness for the bacterial communities with lower abundance in the OWTSs. In addition, 16S sequencing resolved only 48 out of 188 bacterial communities identified by shotgun sequencing (using a 0.1% abundance cutoff). Three key bacterial genera (Desulfomicrobium, Simplicispira, and Phenylobacterium) in anaerobic digestion processes were differentially abundant for both sequencing methods. These data indicate that both sequencing methods provide similar overall profiles for bacterial communities in anaerobic digestor systems. However, shotgun sequencing provides significantly (p-value < 0.01) higher taxonomic richness overall. Thus, shotgun sequencing provides a more robust taxonomic and functional profile that can be used for the optimization of anaerobic digestor systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".