Assessing high-throughput environmental DNA extraction methods for meta-barcode characterization of aquatic microbial communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it