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Record W2580375224 · doi:10.1139/gen-2016-0127

Enhancement of single guide RNA transcription for efficient CRISPR/Cas-based genomic engineering

2017· article· en· W2580375224 on OpenAlexvenueno aff
Kumiko Ui‐Tei, Shohei Maruyama, Yuko Nakano

Bibliographic record

VenueGenome · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCRISPRTrans-activating crRNAGuide RNABiologySubgenomic mRNACas9RNAComputational biologyGenome editingRNA polymerase IIITranscription (linguistics)GeneticsDNAGenome engineeringgenomic DNARNA polymeraseGene

Abstract

fetched live from OpenAlex

Genomic engineering using clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) protein is a promising approach for targeting the genomic DNA of virtually any organism in a sequence-specific manner. Recent remarkable advances in CRISPR/Cas technology have made it a feasible system for use in therapeutic applications and biotechnology. In the CRISPR/Cas system, a guide RNA (gRNA), interacting with the Cas protein, recognizes a genomic region with sequence complementarity, and the double-stranded DNA at the target site is cleaved by the Cas protein. A widely used gRNA is an RNA polymerase III (pol III)-driven single gRNA (sgRNA), which is produced by artificial fusion of CRISPR RNA (crRNA) and trans-activation crRNA (tracrRNA). However, we identified a TTTT stretch, known as a termination signal of RNA pol III, in the scaffold region of the sgRNA. Here, we revealed that sgRNA carrying a TTTT stretch reduces the efficiency of sgRNA transcription due to premature transcriptional termination, and decreases the efficiency of genome editing. Unexpectedly, it was also shown that the premature terminated sgRNA may have an adverse effect of inducing RNA interference. Such disadvantageous effects were avoided by substituting one base in the TTTT stretch.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.548

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 head0.012
GPT teacher head0.279
Teacher spread0.267 · 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
GenreEmpirical

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

Citations14
Published2017
Admission routes1
Has abstractyes

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