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sgRNA design is a crucial step in CRISPR technology, which uses computational methods and bioinformatics tools to select and optimize 20-nucleotide-long RNA sequences to precisely match the target genomic location. The CRISPR-Cas9 system can tolerate multiple sequence mismatches and DNA or RNA bulges, but this can also lead to unintended genomic sites being cleaved, i.e., off-target effects. Although this endonuclease system enables genome editing at nucleotide resolution, a major challenge lies in accurately predicting the on-target efficiency and off-target profile of sgRNAs. Furthermore, selecting an appropriate gRNA design may become more complex for knocking out different types of gene functions, as different gene features such as transcription start sites, co-coding exons, etc. may require different gRNA design strategies.
Creative Biogene's Intelligent sgRNA Design Service utilizes a variety of advanced deep learning models and big data analysis technologies to ensure the optimal sgRNA design solution for each project. By combining the latest advances in machine learning and bioinformatics, we can significantly improve the activity and specificity of sgRNA, thereby improving the success rate and accuracy of gene editing.
Figure 1. The implementation of DeepCRISPR includes these steps (Chuai G, et al., 2018)
Our intelligent sgRNA design service utilizes various advanced deep learning models and big data analysis technologies to ensure the best sgRNA design solution for each project. By combining the latest advances in machine learning and bioinformatics, we are able to significantly improve the activity and specificity of sgRNA, thereby increasing the success rate and accuracy of gene editing. In response to the challenges that may be encountered in gene editing, our services pay special attention to the following aspects:
1. Requirements Analysis and Sequence Input
Users submit target gene sequences and editing requirements, and our system begins analyzing and preparing the data. Customize the design of sgRNA for different editing targets (such as gene knockout, knock-in, or regulation).
2. Data Preprocessing and Model Selection
Standardize the sequence and extract features to ensure the quality of the input data. Select an appropriate deep learning model (such as CNN-SVR, CNN-XG, attention-based CNN, etc.) according to the target gene and editing requirements.
3. sgRNA Design and Prediction
The selected model generates candidate sgRNA sequences and predicts their activity on the target gene. Predict off-target effects of candidate sgRNAs and screen out highly specific sgRNAs.
4. Efficiency Evaluation and Optimization
Evaluate the efficiency of sgRNA through deep learning models (such as DeepSgRNA, CRISPRLearner, etc.) to ensure efficient gene editing. Based on the evaluation results, sgRNA is further optimized to ensure the highest editing success rate.
5. Follow-Up Support and Technical Services
Provide comprehensive technical support, including implementation guidance and subsequent optimization suggestions for sgRNA design. Through our expert team, we help users solve various problems encountered during the gene editing process.
With the Intelligent sgRNA Design Service from Creative Biogene, you can unleash the full potential of CRISPR technology! Our dedication to ongoing optimization and thorough technical assistance guarantees a successful and trouble-free gene editing process from beginning to end. Put your faith in Creative Biogene to transform your gene editing efforts and set the standard for intelligent sgRNA creation.