In silico Method in CRISPR/Cas System: An Expedite and Powerful Booster

Abstract

The CRISPR/Cas system has stood in the center of attention in the last few years as a revolutionary gene editing tool with a wide application to investigate gene functions. However, the labor-intensive workflow requires a sophisticated pre-experimental and post-experimental analysis, thus becoming one of the hindrances for the further popularization of practical applications. Recently, the increasing emergence and advancement of the in silico methods play a formidable role to support and boost experimental work. However, various tools based on distinctive design principles and frameworks harbor unique characteristics that are likely to confuse users about how to choose the most appropriate one for their purpose. In this review, we will present a comprehensive overview and comparisons on the in silico methods from the aspects of CRISPR/Cas system identification, guide RNA design, and post-experimental assistance. Furthermore, we establish the hypotheses in light of the new trends around the technical optimization and hope to provide significant clues for future tools development.

A one-step platform for screening high-efficient and minimal off-target CRISPR/Cas13 crRNAs to eradicate SARS-CoV-2 virus for treatment of COVID-19 patients

“…Therefore, more attention is paid to design an efficient and functional guide RNA with high on-target efficacy and low off-target effects [32] . Three different computation-based genres appear for guide RNA designing: 1. pattern recognition genre; 2. feature rule genre; 3. machine learning genre [33] . In theory, the CRISPR/Cas system searches PAM sequence in genome and guide RNA recognizes target site to activate endonuclease activity to cut specific locus.…”

“…In theory, the CRISPR/Cas system searches PAM sequence in genome and guide RNA recognizes target site to activate endonuclease activity to cut specific locus. However, sequence features of guide RNA, epigenetic features of the host genome, energetics features of guide RNA and the host genome affect guide RNA efficacy, which make guide RNA design become extraordinarily complex [33] . Therefore, guide RNAs need to be experimentally validated for the efficacy after designing by the software.…”

Impact of CRISPR-Cas9-Based Genome Engineering in Farm Animals

“…There are numerous web-based tools available for designing of target-specific sgRNA; each of them has their advantage and disadvantage. The information we have provided in this review will help you to choose the best tool based on your requirement [ 61 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ].…”

“…An algorithm specific to all tools output provides a list of candidate guide RNA sequences comparing predicted off-target sites for each input. Many of them are targeted to provide a guide RNA sequence that minimises the likelihood of off-target effects, but their strategy can be varied, specifically in CHOP-CHOP, which uses empirical data from multiple recent articles to compute efficiency scores [ 67 , 70 ].…”

A Platform to Aid Select the Optimal Tool to Design Guide Rnas

“…The on-target specificity of all these CRISPR-based editing systems is mainly determined by the guiding component, guide RNA [11] . Since a segment of 20 nucleotides can occur multiple times in a given genome, and some mismatches may be accepted by CRISPR/Cas system, off-target could be produced [12] . Meanwhile, the differential editing efficiency of sgRNAs at distinct locations of the same gene, and hence maximizing on-target and minimizing off-target is essential for the application of the CRISPR/Cas system [13] .…”