Expressed sequence tag analysis of the emu (Dromaius novaehollandiae) pituitary by 454 GS Junior pyrosequencing
Bibliographic record
Abstract
Emus (Dromaius novaehollandiae) are farmed for their oil for pharmaceutical and cosmetic uses. This emu pituitary expressed sequence tag study was undertaken to identify novel transcripts in the emu pituitary to propel their identification and functional studies. By mapping reads derived from the Roche 454 GS Junior pyrosequencer to 8 reference species (human, mouse, chicken, zebra finch, fruit fly, turkey, round worm, and Carolina anole lizard) from the UniGene database, a total of 81,788 reads (53,312 mapped reads) were obtained and assembled with Reference Sequence (RefSeq). We annotated 6,676 potential emu genes by referencing 7 species (excluding lizard) and identified 1,232 potential genes common among 3 species (human, mouse, and chicken) with complete available reference genomes. Gene Ontology analysis revealed 376 Gene Ontology terms showing, with the highest counts, their involvements in biological processes, metabolism, and cellular components. These potential genes were detected to associate with 20 pathways including mitogen-activated protein kinase, insulin, neurotrophin signaling pathways, and carbohydrate digestion and absorption pathway. We also revealed a panel of tissue-specific genes including regulator of G-protein signaling protein (RGS), glucagon-like peptide receptor (GLPR), and growth hormone-inducible transmembrane protein (GHITM). Additionally, fatty acid binding protein (FABP), fatty acid desaturase (FAS), and stearoyl-coenzyme A desaturase (SCD), key enzyme genes in fat metabolism, were found to be also expressed in emu pituitary. This expressed sequence tag study represents the first step in functional characterization of emu pituitary gene expression and SNP identification for the improvement of fat production in the emu.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".