Transcript Profiling Provides Evidence of Functional Divergence and Expression Networks among Ribosomal Protein Gene Paralogs in<i>Brassica napus</i>
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Bibliographic record
Abstract
The plant ribosome is composed of 80 distinct ribosomal (r)-proteins. In Arabidopsis thaliana, each r-protein is encoded by two or more highly similar paralogous genes, although only one copy of each r-protein is incorporated into the ribosome. Brassica napus is especially suited to the comparative study of r-protein gene paralogs due to its documented history of genome duplication as well as the recent availability of large EST data sets. We have identified 996 putative r-protein genes spanning 79 distinct r-proteins in B. napus using EST data from 16 tissue collections. A total of 23,408 tissue-specific r-protein ESTs are associated with this gene set. Comparative analysis of the transcript levels for these unigenes reveals that a large fraction of r-protein genes are differentially expressed and that the number of paralogs expressed for each r-protein varies extensively with tissue type in B. napus. In addition, in many cases the paralogous genes for a specific r-protein are not transcribed in concert and have highly contrasting expression patterns among tissues. Thus, each tissue examined has a novel r-protein transcript population. Furthermore, hierarchical clustering reveals that particular paralogs for nonhomologous r-protein genes cluster together, suggesting that r-protein paralog combinations are associated with specific tissues in B. napus and, thus, may contribute to tissue differentiation and/or specialization. Altogether, the data suggest that duplicated r-protein genes undergo functional divergence into highly specialized paralogs and coexpression networks and that, similar to recent reports for yeast, these are likely actively involved in differentiation, development, and/or tissue-specific processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it