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Can Plants Genomes be Edited Using the Current gRNA Ranking Prediction Algorithms?

Fatima Sheikh

Conventional breeding often requires decades to introduce a new feature into a crop, but recently developed genome sequence modification technology offers the potential to shorten this time. One of these cutting-edge breeding techniques uses CRISPR/Cas9, an RNA-directed DNA nuclease, to cut the genomic DNA in living organisms, making it easier to delete or insert sequences. Guide RNAs control this targeting based on certain sequences (gRNAs) [1]. But selecting the best gRNA sequence is not without its difficulties. Although many of them allow the use of plant genomes to identify potential off-target regions, almost all of the current gRNA design tools for usage in plants are based on data from animal experimentation. Here, we analyse the performance and predicted consistency of eight various online gRNA-site tools. Unfortunately, neither a statistically meaningful association between rankings and in vivo effectiveness, nor any agreement between the rankings produced by the various
algorithms. This indicates that significant gRNA performance and/or target site accessibility aspects in plants have not yet been clarified and taken into account by gRNA-site prediction algorithms [2].