Patterns of stability and change in the maize genome: a case study of small RNA transcriptomes in two recombinant inbred lines and their progenitors
Bibliographic record
Abstract
Small RNAs (sRNAs) are epigenetic regulators of eukaryotic genes and transposable elements (TEs). Diverse sRNA expression patterns exist within a species, but how this diversity arises is not well understood. To provide a window into the dynamics of maize sRNA patterning, sRNA and mRNA transcriptomes were examined in two related Zea mays recombinant inbred lines (RILs) and their inbred parents. Analysis of these RILs revealed that most clusters of sRNA expression retained the parental sRNA expression level. However, expression states that differ from the parental allele were also observed, predominantly reflecting decreases in sRNA expression. When RIL sRNA expression differed from the parental allele, the new state was frequently similar between the two RILs, and similar to the expression state found at the allele in the other parent. Novel sRNA expression patterns, distinct from those of either parent, were rare. Additionally, examination of sRNA expression over TEs revealed one TE family, Gyma, which showed consistent enrichment for RIL sRNA expression differences compared to those found in parental alleles. These findings provide insights into how sRNA silencing might evolve over generations and suggest that further investigation into the molecular nature of sRNA trans regulators is warranted.
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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 | 0.001 | 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".