“Maximizing the success of CRISPR gene editing using computational tools”

 

 Feeding a growing global population amid changing climate conditions is a major challenge. Genetic variation is vital for agricultural improvement, making increased productivity and sustainability essential. To ensure future food security, scientific and technological advancements in crop production are necessary. Plant breeding seeks to create and exploit genetic variations, and genome editing techniques like CRISPR (clustered regularly interspaced short palindromic repeats) – Cas (CRISPR-associated) are facilitating precision breeding, shaping the future of plant breeding by introducing precise, predictable modifications for desired traits.¹ Bioinformatics has been crucial in detecting and analyzing CRISPR systems, significantly advancing CRISPR-Cas research. It played a pivotal role in discovering the CRISPR-Cas system and identifying new functions. In 1987, Yoshizumi Ishino first described the signature repeat-spacer architecture of CRISPR arrays. Later, Francisco Mojica, supported by bioinformatics analyses, demonstrated that CRISPR arrays were not only in Escherichia coli but also widespread in most archaeal and many bacterial genomes.²

The past decade has seen rapid advancements in identifying versatile CRISPR/Cas nucleases, their variants, and precise genome editors. These programmable, robust genome editors offer an effective RNA-guided platform for fundamental life science research and applications such as targeted crop improvement. A critical principle is to guide alterations in genomic sequences without undesired off-target effects, relying on the efficiency and specificity of sgRNA-directed recognition. Recent progress in empirical scoring algorithms and machine learning models has improved sgRNA design and off-target prediction.²

In this context, there are several web-based tools available to design sgRNA robustly. A comparison of the web-based tools DeskGen, Benchling, and CRISPR-P shows that they are efficient at designing unique sgRNA and detecting off-target effects.³ Tools like TIDE (Tracking of Indels by DEcomposition) and TIDER (Tracking of Insertions, Deletions, and Recombination events) facilitate the efficient detection of InDels and substitutions created by the CRISPR-Cas system at a lower cost than traditional PCR and enzyme-based assays.⁴

In conclusion, integrating CRISPR with bioinformatics tools has become essential for genome-wide screening and analysis. CRISPR-bioinformatics integration has been employed in genome-wide screening studies to pinpoint genetic variants associated with specific phenotypes and in functional genomics studies to elucidate the functions of specific genes and genetic pathways.

References:

1. CHEN, K., WANG, Y., ZHANG, R., ZHANG, H. AND GAO, C., 2019, CRISPR/Cas genome editing and precision plant breeding in agriculture. Annu. Rev. Plant Biol., 70(1): 667-697.

2. LI, C., CHU, W., GILL, R.A., SANG, S., SHI, Y., HU, X., YANG, Y., ZAMAN, Q.U. AND ZHANG, B., 2023, Computational tools and resources for CRISPR/Cas genome editing. Genom. Proteom. Bioinform., 21(1): 108-126.

3. UNIYAL, A.P., MANSOTRA, K., YADAV, S.K. AND KUMAR, V., 2019, An overview of designing and selection of sgRNAs for precise genome editing by the CRISPR-Cas9 system in plants. 3 Biotech, 9(6): 223.

4. BRINKMAN, E.K. AND VAN STEENSEL, B., 2019, Rapid quantitative evaluation of CRISPR genome editing by TIDE and TIDER. (eds) CRISPR Gene Editing. Methods in Molecular Biology, vol 1961. Humana Press, New York, pp.29-44.