Transcript Profiling Provides Evidence of Functional Divergence and Expression Networks among Ribosomal Protein Gene Paralogs in<i>Brassica napus</i>
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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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 | 0.001 |
| 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) | 0.001 | 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".