Genome‐wide identification of long non‐coding RNAs suggests a potential association with effector gene transcription in <i>Phytophthora sojae</i>
Bibliographic record
Abstract
Numerous long non-coding RNAs (lncRNAs) identified and characterized in mammals, plants and fungi have been found to play critical regulatory roles in biological processes. However, little is known about the role of lncRNAs in oomycete plant pathogens, which cause devastating damage to the economy and ecosystems. We used strand-specific RNA sequencing (RNA-seq) to generate a computational pipeline to identify lncRNAs in Phytophthora sojae, a model oomycete plant pathogen. In total, 940 lncRNAs with 1010 isoforms were identified from RNA-seq data obtained from four representative stages of P. sojae. The lncRNAs had shorter transcript lengths, longer exon lengths, fewer numbers of exons, lower GC content and higher minimum free energy values compared with protein-coding genes. lncRNAs in P. sojae exhibited low sequence conservation amongst oomycetes and P. sojae isolates. Transcriptional data indicated that P. sojae lncRNAs tended to be transcribed in a stage-specific manner; representative lncRNAs were validated by semi-quantitative reverse transcription-polymerase chain reaction. Phytophthora sojae lncRNAs were concentrated in gene-sparse regions, and lncRNAs were associated with secreted protein and effector coding genes. The neighbouring genes of lncRNAs encoded various effector family members, and RNA-seq data revealed a correlation between the transcription level of lncRNAs and their neighbouring genes. Our results provide the first comprehensive identification of lncRNAs in oomycetes and suggest a potential association between lncRNAs and effector genes.
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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 | 0.001 |
| 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) | 0.001 | 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".