Genome-Wide Identification, Characterization and Evolutionary Analysis of Long Intergenic Noncoding RNAs in Cucumber
Bibliographic record
Abstract
Long intergenic noncoding RNAs (lincRNAs) are intergenic transcripts with a length of at least 200 nt that lack coding potential. Emerging evidence suggests that lincRNAs from animals participate in many fundamental biological processes. However, the systemic identification of lincRNAs has been undertaken in only a few plants. We chose to use cucumber (Cucumis sativus) as a model to analyze lincRNAs due to its importance as a model plant for studying sex differentiation and fruit development and the rich genomic and transcriptome data available. The application of a bioinformatics pipeline to multiple types of gene expression data resulted in the identification and characterization of 3,274 lincRNAs. Next, 10 lincRNAs targeted by 17 miRNAs were also explored. Based on co-expression analysis between lincRNAs and mRNAs, 94 lincRNAs were annotated, which may be involved in response to stimuli, multi-organism processes, reproduction, reproductive processes, and growth. Finally, examination of the evolution of lincRNAs showed that most lincRNAs are under purifying selection, while 16 lincRNAs are under natural selection. Our results provide a rich resource for further validation of cucumber lincRNAs and their function. The identification of lincRNAs targeted by miRNAs offers new clues for investigations into the role of lincRNAs in regulating gene expression. Finally, evaluation of the lincRNAs suggested that some lincRNAs are under positive and balancing selection.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".