Genome-wide identification evolution and expression of vestigial-like gene family in chicken
Bibliographic record
Abstract
Vestigial-like (Vgll) genes are widespread in vertebrates and play an important role in muscle development. In this study, we used bioinformatics methods to systematically identify the chicken VGLL family in the whole genome and investigated its evolutionary history and gene structure features. Tissue expression spectra combined with real-time PCR data were used to analyze the organizational expression pattern of the genes. Based on the maximum likelihood method, a phylogenetic tree of the VGLL family was constructed, and 94 VGLL genes were identified in 24 breeds, among which four VGLL family genes were identified in the chicken genome. Ten motifs were detected in the VGLL genes, and the analysis of introns combined with gene structure revealed that the family was conserved during evolution. Tissue expression analysis suggested that the expression profiles of the VGLL family genes in 16 tissues differed between LU Shi and AA broilers. In addition, a single gene (VGLL2) showed increased expression in chickens at embryonic days 10–16 and was involved in the growth and development of skeletal muscle in chickens in the embryonic stage. In summary, VGLL genes are involved in chicken muscle growth and development, which provides useful information for subsequent functional studies of VGLL 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 | 0.000 |
| 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) | 0.000 | 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 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".