Genome-wide identification of long noncoding natural antisense transcripts and their responses to light in <i>Arabidopsis</i>
Bibliographic record
Abstract
Recent research on long noncoding RNAs (lncRNAs) has expanded our understanding of gene transcription regulation and the generation of cellular complexity. Depending on their genomic origins, lncRNAs can be transcribed from intergenic or intragenic regions or from introns of protein-coding genes. We have recently reported more than 6000 intergenic lncRNAs in Arabidopsis. Here, we systematically identified long noncoding natural antisense transcripts (lncNATs), defined as lncRNAs transcribed from the opposite DNA strand of coding or noncoding genes. We found a total of 37,238 sense-antisense transcript pairs and 70% of annotated mRNAs to be associated with antisense transcripts in Arabidopsis. These lncNATs could be reproducibly detected by different technical platforms, including strand-specific tiling arrays, Agilent custom expression arrays, strand-specific RNA-seq, and qRT-PCR experiments. Moreover, we investigated the expression profiles of sense-antisense pairs in response to light and observed spatial and developmental-specific light effects on 626 concordant and 766 discordant NAT pairs. Genes for a large number of the light-responsive NAT pairs are associated with histone modification peaks, and histone acetylation is dynamically correlated with light-responsive expression changes of NATs.
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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 | 0.000 | 0.000 |
| 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".