Bibliographic record
Abstract
Gene expression is a key step in developmental regulation and responses in changing environments in plants. Alternative splicing (AS) is a process generating multiple RNA isoforms from a single gene pre-mRNA transcript that increases the diversity of functional proteins and RNAs. Identification and analysis of alternatively splicing events are critical for crop improvement and understanding regulatory mechanisms. In maize large numbers of transcripts generated by RNA-seq technology are available, we incorporated these data with data assembled with ESTs and mRNAs to comprehensively catalog all genes having pre-mRNAs undergoing AS. A total of 192 624 AS events were detected and classified, including 103 566 (53.8%) basic events and 89 058 (46.2%) complex events which were formed by combination of various types of basic events. Intron retention was the dominant type of basic AS event, accounting for 24.1%. These AS events were identified from 91 128 transcripts which were generated from 26 669 genomic loci, of which consisted of 20 860 gene models. It was estimated that 55.3% maize genes may be subjected to AS. The transcripts mapping information can be used to improve the predicted gene models in maize. The data can be accessed at Plant Alternative Splicing Database (http://proteomics.ysu.edu/altsplice/).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".