Differentially expressed genes during bovine intramuscular adipocyte differentiation profiled by serial analysis of gene expression
Bibliographic record
Abstract
Beef marbling or intramuscular fat deposition is an economically important carcass trait in Japanese Black cattle. To investigate genes involved in intramuscular adipogenesis, differential gene expression during adipogenesis in a clonal bovine intramuscular preadipocyte (BIP) cell line was profiled with serial analysis of gene expression (SAGE). We sequenced 75 283 tags for the proliferation phase (day 0) and 81 878 tags for the differentiation phase (4 days after adipogenic stimulation: day 4). A comparison of the unique SAGE tag frequencies between the day 0- and day 4-libraries revealed that 878 (2.8%) of the 30 989 unique putative transcripts were expressed at significantly different levels (P < 0.05); 401 tags (1.4%) were up-regulated and 477 tags (1.2%) were down-regulated in the day 4-library relative to the day 0-library. We confirmed up-regulation of 10 tags of the genes that were up-regulated in the previous subtraction cloning studies in BIP cells [Animal Science Journal, 76 (2005) 479]. Of the 878 differentially expressed tags, 377 were identified in the bovine RefSeq library and 356 were assigned a bovine draft genomic sequence. Fifteen tags were mapped in previously detected beef marbling quantitative trait loci (QTL) regions [Mammalian Genome, 18 (2007) 125]. These genes may be involved in the adipogenic processes of beef marbling.
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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 | 0.000 | 0.001 |
| 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".