Comparative transcriptomics uncovers alternative splicing changes and signatures of selection from maize improvement
Bibliographic record
Abstract
BACKGROUND: Alternative splicing (AS) is an important regulatory mechanism that greatly contributes to eukaryotic transcriptome diversity. A substantial amount of evidence has demonstrated that AS complexity is relevant to eukaryotic evolution, development, adaptation, and complexity. In this study, six teosinte and ten maize transcriptomes were sequenced to analyze AS changes and signatures of selection in maize domestication and improvement. RESULTS: In maize and teosinte, 13,593 highly conserved genes, including 12,030 multiexonic genes, were detected. By identifying AS isoforms from mutliexonic genes, we found that AS types were not significantly different between maize and teosinte. In addition, the two main AS types (intron retention and alternative acceptor) contributed to more than 60% of the AS events in the two species, but the average unique AS events per each alternatively spliced gene in maize (4.12) was higher than that in teosinte (2.26). Moreover, 94 genes generating 98 retained introns with transposable element (TE) sequences were detected in maize, which is far more than 9 retained introns with TEs detected in teosinte. This indicates that TE insertion might be an important mechanism for intron retention in maize. Additionally, the AS levels of 3864 genes were significantly different between maize and teosinte. Of these, 151 AS level-altered genes that are involved in transcriptional regulation and in stress responses are located in regions that have been targets of selection during maize improvement. These genes were inferred to be putatively improved genes. CONCLUSIONS: We suggest that both maize and teosinte share similar AS mechanisms, but more genes have increased AS complexity during domestication from teosinte to maize. Importantly, a subset of AS level-increased genes that encode transcription factors and stress-responsive proteins may have been selected during maize improvement.
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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) | 0.000 | 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".