Comparison of swab DNA extraction methods for examining sea star dermal microbiomes
Bibliographic record
Abstract
Marine invertebrates are surrounded by and interact with an array of microbes, yet their microbiomes remain largely unexplored. With a seemingly endless choice of nucleic acid extraction kits, there is a need to assess the compatibility across approaches to determine whether microbiome results are comparable across studies employing different extraction methods. In this study, 5 kits were compared for extracting DNA from dermal swabs from 2 sea star species: Pisaster ochraceus and Dermasterias imbricata. DNA yield varied by kit, as did the ease of PCR amplification. Using 16S rRNA amplicon sequencing, differences in microbial richness and diversity were observed between sea star species, but not among extraction kits. Relative abundances of the most abundant prokaryotic phyla were largely attributed to sea star species rather than kit: the D. imbricata microbiome was dominated by Proteobacteria, whereas P. ochraceous had more even representation of Proteobacteria, Spirochaetota and Bacteroidota. Our results suggest that, despite some differences in ease of amplification, all 5 extraction kits examined here provide comparable and suitable results for characterizing sea star dermal microbiomes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".