Potential and limitations of bovine-specific arrays for the analysis of mRNA levels in early development: preliminary analysis using a bovine embryonic array
Bibliographic record
Abstract
New insights into the early development of large mammals are becoming available through the measurement of differential mRNA levels in oocytes and preimplantation embryos. These advances in knowledge are rapidly picking up in pace, mainly owing to the advantages brought by new molecular biology approaches being developed. The possibility of amplifying the starting material and therefore making measurements in single embryo units is now feasible. With these tools, the evaluation of variations in gene expression patterns during the preimplantation period or the impact of culture on mRNA levels is now possible. However, it is important to keep in mind that these methods still have limitations associated with sample preparation or the use of the appropriate controls. Even proper methods of analysis are very important to achieve the full benefit of the application of these tools. The present paper describes some of the potential, as well as limitations, of mRNA level analysis in early embryos, especially for microarray analysis. We have generated a bovine cDNA array (>2000 clones) that contains expressed sequence tags (ESTs) collected from various preimplantation development stages. Using this chip, we have initiated the characterisation of global mRNA level patterns of several key developmental stages from the immature oocyte to the blastocyst stage. As expected, the hybridisation results indicate very different expression profiles involving hundreds of genes when comparing oocyte and blastocyst samples to a reference mRNA sample made from a pool of ESTs from pooled somatic tissues. Although this array is still in its preliminary stage and the EST bank has not been processed to contain only unigenes, it is already a very useful tool for discovering candidate genes that may play important roles during early embryonic life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".