Genes influencing deposition of melanin in breast muscle of the Xuefeng black bone chicken based on bioinformatic analysis
Bibliographic record
Abstract
The Xuefeng black bone chicken (XFBC) represents an important poultry genetic resource. However, the darkness in breast muscle is heterogeneous. The molecular genetic mechanisms underlying melanogenesis of breast muscle in XFBC remains unclear. This study used RNA-seq to compare the difference in transcriptome between hyperpigmentation and hypopigmentation of breast muscle. Six cDNA libraries were constructed for hyperpigmentation and hypopigmentation groups in XFBC. We identified 395 differently expressed genes (DEGs) between hyperpigmentation and hypopigmentation group ( P < 0.05, |log2FC|≥1). Gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated several differentially enriched biological functions and pathways involved in melanogenesis of the breast muscle. Gene set enrichment analysis (GSEA) GO analysis identified two significant gene sets, including the pathways of pigment metabolic process and transmembrane receptor protein tyrosine kinase activity. GSEA–KEGG analysis identified the process of tyrosine metabolism and several genes related with melanogenesis in breast muscle of the XFBC. The protein–protein interaction network was constructed and eight genes were clustered in the module. We identified nine hub genes, including TYR, TYRP1, DCT, GPR143, MLANA, SLC24A5, GPNMB, MLPH, and EDNRB2. Taken together, the DEGs and hub genes identified in the study provide a solid basis for the study of the genetic regulatory mechanisms involved the melanogenesis in the breast muscle of the XFBC.
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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 | 0.001 | 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".