Genome-Wide Characterization of Serine/Arginine-Rich Gene Family and Its Genetic Effects on Agronomic Traits of Brassica napus
Bibliographic record
Abstract
Serine/arginine-rich (SR) proteins are indispensable factors for RNA splicing, and they play important roles in development and abiotic stress responses. However, little information on SR genes in Brassica napus is available. In this study, 59 SR genes were identified and classified into seven subfamilies: SR, SCL, RS2Z, RSZ, RS, SR45, and SC. In each subfamily, the genes showed relatively conserved structures and motifs, but displayed distinct expression patterns in different tissues and under abiotic stress, which might be caused by the varied cis-acting regulatory elements among them. Transcriptome datasets from Pacbio/Illumina platforms showed that alternative splicing of SR genes was widespread in B. napus and the majority of paralogous gene pairs displayed different splicing patterns. Protein-protein interaction analysis indicated that SR proteins were involved in the regulation of the whole lifecycle of mRNA, from synthesis to decay. Moreover, the association mapping analysis suggested that 12 SR genes were candidate genes for regulating specific agronomic traits, which indicated that SR genes could affect the development and hence influence the important agronomic traits of B. napus. In summary, this study provided elaborate information on SR genes in B. napus, which will aid further functional studies and genetic improvement of agronomic traits in B. napus.
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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".