Computational Tools and Resources for CRISPR/Cas Genome Editing

The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants, as well as in developing precise CRISPR/Cas-derived genome editors. The programmab...

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Bibliographic Details
Published in:Genomics, proteomics & bioinformatics proteomics & bioinformatics, 2023-02, Vol.21 (1), p.108-126
Main Authors: Li, Chao, Chu, Wen, Gill, Rafaqat Ali, Sang, Shifei, Shi, Yuqin, Hu, Xuezhi, Yang, Yuting, Zaman, Qamar U., Zhang, Baohong
Format: Article
Language:English
Subjects:
Algorithm
Computational tool
CRISPR/Cas9
Efficiency and specificity
Genome editing
Resource Review
sgRNA
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Summary:The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants, as well as in developing precise CRISPR/Cas-derived genome editors. The programmable and robust features of the genome editors provide an effective RNA-guided platform for fundamental life science research and subsequent applications in diverse scenarios, including biomedical innovation and targeted crop improvement. One of the most essential principles is to guide alterations in genomic sequences or genes in the intended manner without undesired off-target impacts, which strongly depends on the efficiency and specificity of single guide RNA (sgRNA)-directed recognition of targeted DNA sequences. Recent advances in empirical scoring algorithms and machine learning models have facilitated sgRNA design and off-target prediction. In this review, we first briefly introduce the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purposes. Secondly, we focus on the computer-assisted tools and resources that are widely used in designing sgRNAs and analyzing CRISPR/Cas-induced on- and off-target mutations. Thirdly, we provide insights into the limitations of available computational tools that would help researchers of this field for further optimization. Lastly, we suggest a simple but effective workflow for choosing and applying web-based resources and tools for CRISPR/Cas genome editing.
ISSN:1672-0229
2210-3244
DOI:10.1016/j.gpb.2022.02.006