A beginner's guide to understanding and implementing the genetic modification of zebrafish
Bibliographic record
Abstract
Zebrafish are a relevant and useful vertebrate model species to study normal- and patho-physiology, including that of the heart, due to conservation of protein-coding genes, organ system organisation and function, and efficient breeding and housing. Their amenability to genetic modification, particularly compared to other vertebrate species, is another great advantage, and is the focus of this review. A vast number of genetically engineered zebrafish lines and methods for their creation exist, but their incorporation into research programs is hindered by the overwhelming amount of technical details. The purpose of this paper is to provide a simplified guide to the fundamental information required by the uninitiated researcher for the thorough understanding, critical evaluation, and effective implementation of genetic approaches in the zebrafish. First, an overview of existing zebrafish lines generated through large scale chemical mutagenesis, retroviral insertional mutagenesis, and gene and enhancer trap screens is presented. Second, descriptions of commonly-used genetic modification methods are provided including Tol2 transposon, TALENs (transcription activator-like effector nucleases), and CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9). Lastly, design features of genetic modification strategies such as promoters, fluorescent reporters, and conditional transgenesis, are summarised. As a comprehensive resource containing both background information and technical notes of how to obtain or generate zebrafish, this review compliments existing resources to facilitate the use of genetically-modified zebrafish by researchers who are new to the field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.033 |
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".