Are the current gRNA ranking prediction algorithms useful for genome editing in plants?
Introducing a new trait into a crop through conventional breeding commonly takes decades, but recently developed genome sequence modification technology has the potential to accelerate this process. One of these new breeding technologies relies on an RNA-directed DNA nuclease (CRISPR/Cas9) to cut th...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Online Access: | http://hdl.handle.net/20.500.11937/78228 |
| _version_ | 1848763943652163584 |
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| author | Eftekhari, Fatima Shand, Kylie Hayashi, Satomi O'Brien, Martin McGree, James Johnson, Alexander AT Dugdale, Benjamin Waterhouse, Peter M |
| author_facet | Eftekhari, Fatima Shand, Kylie Hayashi, Satomi O'Brien, Martin McGree, James Johnson, Alexander AT Dugdale, Benjamin Waterhouse, Peter M |
| author_sort | Eftekhari, Fatima |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Introducing a new trait into a crop through conventional breeding commonly takes decades, but recently developed genome sequence modification technology has the potential to accelerate this process. One of these new breeding technologies relies on an RNA-directed DNA nuclease (CRISPR/Cas9) to cut the genomic DNA, in vivo, to facilitate the deletion or insertion of sequences. This sequence specific targeting is determined by guide RNAs (gRNAs). However, choosing an optimum gRNA sequence has its challenges. Almost all current gRNA design tools for use in plants are based on data from experiments in animals, although many allow the use of plant genomes to identify potential off-target sites. Here, we examine the predictive uniformity and performance of eight different online gRNA-site tools. Unfortunately, there was little consensus among the rankings by the different algorithms, nor a statistically significant correlation between rankings and in vivo effectiveness. This suggests that important factors affecting gRNA performance and/or target site accessibility, in plants, are yet to be elucidated and incorporated into gRNA-site prediction tools. |
| first_indexed | 2025-11-14T11:11:30Z |
| format | Journal Article |
| id | curtin-20.500.11937-78228 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | eng |
| last_indexed | 2025-11-14T11:11:30Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-782282021-07-05T08:07:53Z Are the current gRNA ranking prediction algorithms useful for genome editing in plants? Eftekhari, Fatima Shand, Kylie Hayashi, Satomi O'Brien, Martin McGree, James Johnson, Alexander AT Dugdale, Benjamin Waterhouse, Peter M Introducing a new trait into a crop through conventional breeding commonly takes decades, but recently developed genome sequence modification technology has the potential to accelerate this process. One of these new breeding technologies relies on an RNA-directed DNA nuclease (CRISPR/Cas9) to cut the genomic DNA, in vivo, to facilitate the deletion or insertion of sequences. This sequence specific targeting is determined by guide RNAs (gRNAs). However, choosing an optimum gRNA sequence has its challenges. Almost all current gRNA design tools for use in plants are based on data from experiments in animals, although many allow the use of plant genomes to identify potential off-target sites. Here, we examine the predictive uniformity and performance of eight different online gRNA-site tools. Unfortunately, there was little consensus among the rankings by the different algorithms, nor a statistically significant correlation between rankings and in vivo effectiveness. This suggests that important factors affecting gRNA performance and/or target site accessibility, in plants, are yet to be elucidated and incorporated into gRNA-site prediction tools. 2020 Journal Article http://hdl.handle.net/20.500.11937/78228 10.1371/journal.pone.0227994 eng http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | Eftekhari, Fatima Shand, Kylie Hayashi, Satomi O'Brien, Martin McGree, James Johnson, Alexander AT Dugdale, Benjamin Waterhouse, Peter M Are the current gRNA ranking prediction algorithms useful for genome editing in plants? |
| title | Are the current gRNA ranking prediction algorithms useful for genome editing in plants? |
| title_full | Are the current gRNA ranking prediction algorithms useful for genome editing in plants? |
| title_fullStr | Are the current gRNA ranking prediction algorithms useful for genome editing in plants? |
| title_full_unstemmed | Are the current gRNA ranking prediction algorithms useful for genome editing in plants? |
| title_short | Are the current gRNA ranking prediction algorithms useful for genome editing in plants? |
| title_sort | are the current grna ranking prediction algorithms useful for genome editing in plants? |
| url | http://hdl.handle.net/20.500.11937/78228 |