Sequencing and characterization of lncRNAs in the breast muscle of Gushi and Arbor Acres chickens
Bibliographic record
Abstract
Chicken muscle quality is one of the most important factors determining the economic value of poultry, and muscle development and growth are affected by genetics, environment, and nutrition. However, little is known about the molecular regulatory mechanisms of long non-coding RNAs (lncRNAs) in chicken skeletal muscle development. Our study aimed to better understand muscle development in chickens and thereby improve meat quality. In this study, Ribo-Zero RNA-Seq was used to investigate differences in the expression profiles of muscle development related genes and associated pathways between Gushi (GS) and Arbor Acres (AA) chickens. We identified two muscle tissue specific expression lncRNAs. In addition, the target genes of these lncRNAs were significantly enriched in certain biological processes and molecular functions, as demonstrated by Gene Ontology (GO) analysis, and these target genes participate in five signaling pathway, as revealed by an analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Taken together, these data suggest that different lncRNAs might be involved in regulating chicken muscle development and growth and provide new insight into the molecular mechanisms of lncRNAs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".