Motoche-Monar, C.; Ordoñez, J.E.; Chang, O.; Gonzales-Zubiate, F.A. gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement. Biomolecules2023, 13, 1698.
Motoche-Monar, C.; Ordoñez, J.E.; Chang, O.; Gonzales-Zubiate, F.A. gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement. Biomolecules 2023, 13, 1698.
Motoche-Monar, C.; Ordoñez, J.E.; Chang, O.; Gonzales-Zubiate, F.A. gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement. Biomolecules2023, 13, 1698.
Motoche-Monar, C.; Ordoñez, J.E.; Chang, O.; Gonzales-Zubiate, F.A. gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement. Biomolecules 2023, 13, 1698.
Abstract
In the last decade, the genetic engineering world has been shaken up by a relatively new genetic editing tool based on RNA-guided Nucleases (RGNs): the CRISPR/Cas9 system. Since the first report in 1987 and its characterization in 2007 as a bacterial defense mechanism, the interest and research on this system have grown exponentially. CRISPR systems provide immunity to bacteria against invading genetic material; however, with specific modifications in sequence and structure, it becomes a precise editing system that makes it possible to genetically modify almost any organism. There are diverse approaches regarding the refinement of these modifications, such as constructing more accurate nucleases, understanding the cellular context and facing the epigenetic conditions, or re-designing guide RNAs (gRNAs). Considering the critical importance for the correct CRISPR/Cas9 systems performance, our scope will emphasize in the latter approach. Hence, we present an overview of the past and the most recent guide RNA web-based design tools, highlighting their computational architecture and gRNA characteristics evolution through the years. Our study concisely explains the computational approaches that use machine learning techniques, deep neural networks, and large datasets of gRNA/target interactions to make possible both predictions and classifications directed to design, optimize, and create promising gRNAs suitable for future gene therapies.
Keywords
CRISPR/Cas9; machine learning; gRNA; neural networks; deep learning
Subject
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
