ReviewMolecular genetics and genomics : MGG2021
Can genetic engineering-based methods for gene function identification be eclipsed by genome editing in plants? A comparison of methodologies.
Review in Molecular genetics and genomics : MGG, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- CRISPR-Based genome editing in pulses: current approaches, challenges, and future prospects.Plant molecular biology · 2025Review
- The Emerging Applications of Artificial MicroRNA-Mediated Gene Silencing in Plant Biotechnology.Non-coding RNA · 2025Review
- Recalcitrance to transformation, a hindrance for genome editing of legumes.Frontiers in genome editing · 2023Review
- CRISPR/Cas9 in Planta Hairy Root Transformation: A Powerful Platform for Functional Analysis of Root Traits in Soybean.Plants (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Finding and explaining the functions of genes in plants have promising applications in crop improvement and bioprospecting and hence, it is important to compare various techniques available for gene function identification in plants. Today, the most popular technology among researchers to identify the functions of genes is the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9)-based genome editing method. But by no means can we say that CRISPR/Cas9 is the go-to method for all purposes. It comes with its own baggage. Researchers will agree and have lived through at least seven more technologies deployed to find the functions of genes, which come under three umbrellas: 1. genetic engineering, 2. transient expression, and 3. chemical/physical mutagenesis. Each of the methods evolved when the previous one ran into an insurmountable problem. In this review, we compare the eight technologies against one another on 14 parameters. This review lays bare the pros and cons, and similarities and dissimilarities of various methods. Every method comes with its advantages and disadvantages. For example, the CRISPR/Cas9-based genome editing is an excellent method for modifying gene sequences, creating allelic versions of genes, thereby aiding the understanding of gene function. But it comes with the baggage of unwanted or off-target mutations. Then, we have methods based on random or targeted knockout of the gene, knockdown, and overexpression of the gene. Targeted disruption of genes is required for complete knockout of gene function, which may not be accomplished by editing. We have also discussed the strategies to overcome the shortcomings of the targeted gene-knockout and the CRISPR/Cas9-based methods. This review serves as a comprehensive guide towards the understanding and comparison of various technologies available for gene function identification in plants and hence, it will find application for crop improvement and bioprospecting related research.
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What OpenQuestion holds
Registered trials
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.