Reduced representation bisulfite sequencing (RRBS) analysis reveals variation in distribution and levels of DNA methylation in white birch (<i>Betula papyrifera</i>) exposed to nickel
Bibliographic record
Abstract
Research in understanding the role of genetics and epigenetics in plant adaptations to environmental stressors such as metals is still in its infancy. The objective of the present study is to assess the effect of nickel on DNA methylation level and distribution in white birch ( Betula papyrifera Marshall) using reduced representation bisulfite sequencing (RRBS). The distribution of methylated C sites of each sample revealed that the level of methylation was much higher in CG context varying between 54% and 65%, followed by CHG (24%–31.5%), and then CHH with the methylation rate between 3.3% and 5.2%. The analysis of differentially methylated regions (DMR) revealed that nickel induced both hypermethylation and hypomethylation when compared to water. Detailed analysis showed for the first time that nickel induced a higher level of hypermethylation compared to controls, while potassium triggers a higher level of hypomethylation compared to nickel. Surprisingly, the analysis of the distribution of DMRs revealed that 38%–42% were located in gene bodies, 20%–24% in exon, 19%–20% in intron, 16%–17% in promoters, and 0.03%–0.04% in transcription start site. RRBS was successful in detecting and mapping DMR in plants exposed to nickel.
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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 | 0.000 | 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 | 0.000 |
| 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 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".