Assessing high-throughput environmental DNA extraction methods for meta-barcode characterization of aquatic microbial communities
Bibliographic record
Abstract
The characterization of microbial community dynamics using genomic methods is rapidly expanding, impacting many fields including medical, ecological, and environmental research and applications. One of the biggest challenges for such studies is the isolation of environmental DNA (eDNA) from a variety of samples, diverse microbes, and widely variable community compositions. The current study developed environmentally friendly, user safe, economical, and high throughput eDNA extraction methods for mixed aquatic microbial communities and tested them using 16 s rRNA gene meta-barcoding. Five different lysis buffers including (1) cetyltrimethylammonium bromide (CTAB), (2) digestion buffer (DB), (3) guanidinium isothiocyanate (GITC), (4) sucrose lysis (SL), and (5) SL-CTAB, coupled with four different purification methods: (1) phenol-chloroform-isoamyl alcohol (PCI), (2) magnetic Bead-Robotic, (3) magnetic Bead-Manual, and (4) membrane-filtration were tested for their efficacy in extracting eDNA from recreational freshwater samples. Results indicated that the CTAB-PCI and SL-Bead-Robotic methods yielded the highest genomic eDNA concentrations and succeeded in detecting the core microbial community including the rare microbes. However, our study recommends the SL-Bead-Robotic eDNA extraction protocol because this method is safe, environmentally friendly, rapid, high-throughput and inexpensive.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".