CRISPR/Cas9 Application for Gene Copy Fate Survey of Polyploid Vertebrates
Bibliographic record
Abstract
Polyploidization occurs widely in eukaryotes, and especially in plants. Polyploid plants and some fishes have been commercialized. Typically, severe genomic perturbations immediately follows polyploidization and little is known about how polyploid offspring survive the genetic and epigenetic changes. Investigations into this require the identification of genes related to polyploidization and the discrimination of dosage-balance from paternal and maternal copies, and regardless of the mechanism being either autopolyploidization or allopolyploidization. New approaches and technologies may discern the mosaic of novel gene functions gained through the recombination of paternal and maternal genes in allopolyploidization. Modifications of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) with CRISPR-associated system (Cas) protein 9 (CRISPR/Cas9) have been employed in studies of polyploidization of plants. However, the approach has seldom been applied to polyploidization in vertebrates. Herein, we use CRISPR/Cas9 to trace gene-fate in tetraploid goldfish, and specifically to identify the functional differentiation of two divergent copies of fgf20a, which are expressed differently throughout embryonic development. We expect this gene editing system will be applicable to studies of polyploids and the genetic improvement of polyploid livestock.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".