Combining resources to obtain a comprehensive survey of the bovine embryo transcriptome through deep sequencing and microarrays
Bibliographic record
Abstract
While most assisted reproductive technologies (ART) are considered routine for the reproduction of species of economical importance, such as the bovine, the impact of these manipulations on the developing embryo remains largely unknown. In an effort to obtain a comprehensive survey of the bovine embryo transcriptome and how it is modified by ART, resources were combined to design an embryo-specific microarray. Close to one million high-quality reads were produced from subtracted bovine embryo libraries using Roche 454 Titanium deep sequencing technology, which enabled the creation of an augmented bovine genome catalog. This catalog was enriched with bovine embryo transcripts, and included newly discovered indel type and 3'UTR variants. Using this augmented bovine genome catalog, the EmbryoGENE Bovine Microarray was designed and is composed of a total of 42,242 probes, including 21,139 known reference genes; 9,322 probes for novel transcribed regions (NTRs); 3,677 alternatively spliced exons; 3,353 3'-tiling probes; and 3,723 controls. A suite of bioinformatics tools was also developed to facilitate microrarray data analysis and database creation; it includes a quality control module, a Laboratory Information Management System (LIMS) and microarray analysis software. Results obtained during this study have already led to the identification of differentially expressed blastocyst targets, NTRs, splice variants of the indel type, and 3'UTR variants. We were able to confirm microarray results by real-time PCR, indicating that the EmbryoGENE bovine microarray has the power to detect physiologically relevant changes in gene expression.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".