Methodology significantly affects genome size estimates: Quantitative evidence using bryophytes
Bibliographic record
Abstract
Flow cytometry (FCM) is commonly used to determine plant genome size estimates. Methodology has improved and changed during the past three decades, and researchers are encouraged to optimize protocols for their specific application. However, this step is typically omitted or undescribed in the current plant genome size literature, and this omission could have serious consequences for the genome size estimates obtained. Using four bryophyte species (Brachythecium velutinum, Fissidens taxifolius, Hedwigia ciliata, and Thuidium minutulum), three methodological approaches to the use of FCM in plant genome size estimation were tested. These included nine different buffers (Baranyi's, de Laat's, Galbraith's, General Purpose, LB01, MgSO(4), Otto's, Tris.MgCl(2), and Woody Plant), seven propidium iodide (PI) staining periods (5, 10, 15, 20, 45, 60, and 120 min), and six PI concentrations (10, 25, 50, 100, 150, and 200 microg ml(-1)). Buffer, staining period and staining concentration all had a statistically significant effect (P = 0.05) on the genome size estimates obtained for all four species. Buffer choice and PI concentration had the greatest effect, altering the 1C-values by as much as 8% and 14%, respectively. As well, the quality of the data varied with the different methodology used. Using the methodology determined to be the most accurate in this study (LB01 buffer and PI staining for 20 min at 150 microg ml(-1)), three new genome size estimates were obtained: B. velutinum: 0.46 pg, H. ciliata: 0.30 pg, and T. minutulum: 0.46 pg. While the peak quality of flow cytometry histograms is important, researchers must consider that changes in methodology can also affect the relative peak positions and therefore the genome size estimates obtained for plants using FCM.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".