On the utility of DNA barcoding for species differentiation among brown macroalgae (Phaeophyceae) including a novel extraction protocol
Bibliographic record
Abstract
SUMMARY The generation of a species‐rich DNA barcode database in combination with rapid and affordable sequencing techniques will dramatically change specimen identification in ecological, biogeographical and taxonomic applications. Though cytochrome c oxidase 1 has been shown to be a useful tool for differentiating some groups of marine algae, its wide application in the Phaeophyceae has yet to be studied. The presence of polymerase chain reaction (PCR) inhibiting compounds in members of the Fucales, Laminariales and Tilopteridales, that are often co‐extracted with DNA, has hampered the rapid processing associated with barcode projects. Polyphenolics and polysaccharides are present in concentrations such that DNA extraction methods typically include extensive series of washes, organelle extractions and/or cesium columns. In this paper we examine the utility of cytochrome c oxidase 1 for barcoding the Phaeophyceae and present a method for extracting PCR friendly DNA from brown macroalgae in about 2 h, dramatically reducing the time required from previous methods, some of which take days. This method is easily adapted to a 96 well, high‐throughput format and may have applications in other organisms where the presence of similar PCR inhibiting compounds hinders molecular analyses. We extracted DNA from 106 isolates representing 29 species from 20 genera in nine families from five orders of Phaeophyceae. We were able to amplify the barcode marker (cytochrome c oxidase 1) from all samples and a nuclear marker (internal transcribed spacer region) from 54 selected samples. Cytochrome c oxidase 1 was able to differentiate clearly among species, showing within species divergence of 0.00–0.46%, with the exception of one previously studied genus, and between species divergences of greater than 3%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".