An in-silico comparative analysis of lncRNA expression and their role in the pathogenesis of representative fungal, bacterial and viral infections in rice
Bibliographic record
Abstract
Abstract Long non-coding RNAs (lncRNAs) perform prominent role in the regulation of gene expression during plant development and stress response by directly interacting with DNA, RNA, proteins, and/or triggering production of small regulatory RNA molecules. The objective of our study is to understand the systems-level response of the same plant species to highly diverse pathogens across different kingdoms and evaluate the patterns of similarity vs differences, specifically in the context of lncRNA’s. Towards this objective, we performed a comparative in silico analysis of lncRNA’s of Rice that are differentially expressed in response to infection by bacteria ( Xanthomonas oryzae ), fungus ( Magnaporthe oryzae ) and virus ( Rice black dwarf virus ). Using a tailored lncRNA analysis pipeline, we successfully identified 1125, 719 and 240 lncRNAs in Xanthomonas oryzae infection susceptible cultivar CT9737-6-1-3P-M, Magnaporthe oryzae susceptible LTH accession, and Rice black streaked dwarf virus susceptible Wuyujing No. 7 rice cultivars respectively. The in-silico predicted Cis- and Trans-target genes of lncRNAs were subsequently used to identify the pathways modulated by these lncRNA and how they cluster into unique categories of plant responses to pathogen infections. To further substantiate the role of predicted lncRNA’s in plant defence and immune response our analysis finds that many of the lncRNAs co-localize with the QTLs associated with Blast and Bacterial blight resistance in rice. Our in silico analysis provides a list of common and unique pathogen specific lncRNAs that can provide vital insights into the generic vs tailored mechanisms adopted by rice in different infection scenarios.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".