Transcriptome Analysis Indicates Considerable Divergence in Alternative Splicing Between Duplicated Genes in<i>Arabidopsis thaliana</i>
Bibliographic record
Abstract
Gene and genome duplication events have created a large number of new genes in plants that can diverge by evolving new expression profiles and functions (neofunctionalization) or dividing extant ones (subfunctionalization). Alternative splicing (AS) generates multiple types of mRNA from a single type of pre-mRNA by differential intron splicing. It can result in new protein isoforms or downregulation of gene expression by transcript decay. Using RNA-seq, we investigated the degree to which alternative splicing patterns are conserved between duplicated genes in Arabidopsis thaliana. Our results revealed that 30% of AS events in α-whole-genome duplicates and 33% of AS events in tandem duplicates are qualitatively conserved within leaf tissue. Loss of ancestral splice forms, as well as asymmetric gain of new splice forms, may account for this divergence. Conserved events had different frequencies, as only 31% of shared AS events in α-whole-genome duplicates and 41% of shared AS events in tandem duplicates had similar frequencies in both paralogs, indicating considerable quantitative divergence. Analysis of published RNA-seq data from nonsense-mediated decay (NMD) mutants indicated that 85% of α-whole-genome duplicates and 89% of tandem duplicates have diverged in their AS-induced NMD. Our results indicate that alternative splicing shows a high degree of divergence between paralogs such that qualitatively conserved alternative splicing events tend to have quantitative divergence. Divergence in AS patterns between duplicates may be a mechanism of regulating expression level divergence.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".