Evidence map›Paper›PMID 38136570›Full record

ReviewBiomolecules2023

gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement.

Cristofer Motoche-Monar, Julián E Ordoñez, Oscar Chang, Fernando A Gonzales-Zubiate

Abstract readReview
In one paragraph

Review in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. 10 years of CRISPR/CAS genomic engineering in Yarrowia lipolytica.Bioprocess and biosystems engineering · 2026
    Review
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Cristofer Motoche-MonarSchool of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador.ORCID 0000-0002-0819-5848
Julián E OrdoñezSchool of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador.ORCID 0009-0004-2313-8173
Oscar ChangDepartamento de Electrónica, Universidad Simon Bolivar, Caracas 1080, Venezuela.ORCID 0000-0002-4336-7545
Fernando A Gonzales-ZubiateSchool of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador.ORCID 0000-0001-9519-4079

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past decade, genetic engineering has witnessed a revolution with the emergence of a relatively new genetic editing tool based on RNA-guided nucleases: the CRISPR/Cas9 system. Since the first report in 1987 and characterization in 2007 as a bacterial defense mechanism, this system has garnered immense interest and research attention. CRISPR systems provide immunity to bacteria against invading genetic material; however, with specific modifications in sequence and structure, it becomes a precise editing system capable of modifying the genomes of a wide range of organisms. The refinement of these modifications encompasses diverse approaches, including the development of more accurate nucleases, understanding of the cellular context and epigenetic conditions, and the re-designing guide RNAs (gRNAs). Considering the critical importance of the correct performance of CRISPR/Cas9 systems, our scope will emphasize the latter approach. Hence, we present an overview of the past and the most recent guide RNA web-based design tools, highlighting the evolution of their computational architecture and gRNA characteristics over the years. Our study explains computational approaches that use machine learning techniques, neural networks, and gRNA/target interactions data to enable predictions and classifications. This review could open the door to a dynamic community that uses up-to-date algorithms to optimize and create promising gRNAs, suitable for modern CRISPR/Cas9 engineering.

Indexed as

CRISPR-Cas SystemsRNA, Guide, CRISPR-Cas SystemsAlgorithmsGene EditingMachine LearningRNA, Guide, CRISPR-Cas SystemsCRISPR/Cas9deep learninggRNAmachine learningneural networks

Identifiers

PMID38136570
PMCPMC10741458

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.