cDNA Subtraction and Cloning in the Field of Trophoblast/Placental Development
Bibliographic record
Abstract
cDNA subtractive hybridization is a very powerful method to compare gene expression profiles between two cell or tissue samples of interest. The sample in which differentially expressed transcripts are to be found is usually referred to as “tester,” whereas the reference sample is called “driver.” A cDNA subtraction experiment should always be performed in both directions, with each sample serving as the tester and the driver in separate reactions. The general requirements to perform a cDNA subtraction and cloning procedure encompass the isolation of total RNA or mRNA from the specimens of interest, followed by reverse transcription of the mRNA into cDNA. The concept of cDNA subtraction is based on the hybridization of reverse-transcribed mRNAs present in both experimental samples. These sequences can form double-stranded hybrids that are then removed from the reaction. Only cDNAs from the tester population that remain single-stranded are further amplified, thus representing the pool of differentially expressed genes. This pool enriched for differentially expressed sequences can easily be cloned to generate a subtraction library. The analysis of subtraction efficiency is a crucial step after the procedure in order to rely on the results obtained. The subtractive hybridization is very valuable to achieve insights into genes differentially regulated between two samples of interest. Importantly, however, the generated cDNA pools enriched for genes overexpressed in either of the two tissue or cell specimens are also excellent probes for array hybridization applications—timely methods to identify gene expression changes on a large scale. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".