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New Developments in CRISPR/Cas-based Functional Genomics and their Implications for Research Using Zebrafish

2017· review· en· W2770152187 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCurrent Gene Therapy · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsCRISPRZebrafishFunctional genomicsGenomicsComputational biologyBiologyGeneticsComputer scienceGenomeGene

Abstract

fetched live from OpenAlex

INTRODUCTION: Genome editing using CRISPR/Cas9 has advanced very rapidly in its scope, versatility and ease of use. Zebrafish (Danio rerio) has been one of the vertebrate model species where CRISPR/Cas9 has been applied very extensively for many different purposes and with great success. In particular, disease modeling in zebrafish is useful for testing specific gene variants for pathogenicity in a preclinical setting. Here we describe multiple advances in diverse species and systems that can improve genome editing in zebrafish. OBJECTIVE: To achieve temporal and spatial precision of genome editing, many new technologies can be applied in zebrafish such as artificial transcription factors, drug-inducible or optogenetically-driven expression of Cas9, or chemically-inducible activation of Cas9. Moreover, chemically- or optogenetically- inducible reconstitution of dead Cas9 (catalytically inactive, dCas9) can enable spatiotemporal control of gene regulation. In addition to controlling where and when genome editing occurs, using oligonucleotides allows for the introduction (knock-in) of precise modifications of the genome. CONCLUSION: We review recent trends to improve the precision and efficiency of oligo-based point mutation knock-ins and discuss how these improvements can apply to work in zebrafish. Similarly to how chemical mutagenesis enabled the first genetic screens in zebrafish, multiplexed sgRNA libraries and Cas9 can enable the next revolutionary transition in how genetic screens are performed in this species. We discuss the first examples and prospects of approaches using sgRNAs as specific and effective mutagens. Moreover, we have reviewed methods aimed at measuring the phenotypes of single cells after their mutagenic perturbation with vectors encoding individual sgRNAs. These methods can range from different cell-based reporters to single-cell RNA sequencing and can serve as great tools for high-throughput genetic screens.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.369
GPT teacher head0.523
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it