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Record W3013975168 · doi:10.1007/978-1-0716-0294-2_14

Application of CRISPR/Cas9-Mediated Genome Editing in Leishmania

2020· article· en· W3013975168 on OpenAlexafffund
Wen‐Wei Zhang, Patrick Lypaczewski, Greg Matlashewski

Bibliographic record

VenueMethods in molecular biology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill Genome CentreMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsCRISPRGenome editingBiologyCas9Guide RNAGeneLeishmaniaGenomeComputational biologyTrans-activating crRNAGenetics

Abstract

fetched live from OpenAlex

CRISPR-Cas9 is an RNA guided endonuclease derived from the bacterium Streptococcus pyogenes. Due to its simplicity, versatility, and high efficiency, it has been widely used for genome editing in a variety of organisms including the protozoan parasite Leishmania, the causative agent of human leishmaniasis. Compared to the traditional homologous recombination gene targeting method, CRISPR-Cas9 has been shown to be a more efficient method to delete or disrupt Leishmania genes, generate point mutations, and add tags to endogenous genes. Notably, the stable CRISPR expression systems were shown to delete multicopy family Leishmania genes and genes present in multiploid chromosomes, identify essential Leishmania genes, and create specific chromosome translocations. In this chapter, we describe detailed procedures on using the stable CRISPR expression system for genome editing in Leishmania. These procedures include CRISPR targeting site selection, gRNA design, cloning single and double gRNA coding sequences into the Leishmania CRISPR vector pLdCN, oligonucleotide donor and drug resistance selection donor design, Leishmania cell transfection, screening, and isolation of CRISPR-edited mutants. As the principles of gene editing are generally similar, many of these procedures could also apply to the transient Leishmania CRISPR systems described by other labs.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.597
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.013
GPT teacher head0.390
Teacher spread0.377 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations22
Published2020
Admission routes2
Has abstractyes

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