Intelligent Editing Result Prediction Service

The editing efficiency of different genomic sites varies greatly, and these differences may be caused by factors such as the consensus sequence preference of the deaminase and the binding efficiency of sgRNA to the protospacer. Prolonged exposure time can increase low editing rates, but may result in unwanted "bystander" editing. By accurately predicting the editing results, the most effective sgRNA sequences can be selected and optimized to improve the editing efficiency of the target gene. This helps ensure that gene-editing tools make efficient genome modifications at the intended locations, saving time and resources.

Our intelligent editing result prediction service utilizes the most advanced deep learning technology and genomic data analysis to provide accurate editing result prediction and evaluation for gene editing projects. Whether in the fields of basic research, drug development, or agricultural biotechnology, our services can help customers quickly and reliably evaluate editing effects and guide subsequent experimental design and decision-making.

The figure 1 illustrates an attention-based deep learning algorithm designed to predict base editing probabilities, featuring embedding, encoder, and output blocks to process the target sequence and return a confidence score for base conversions. (doi: 10.1038/s41467-021-25375-z) Figure 1. A machine learning model for predicting base editing outcomes. (Marquart, K. F., et al., 2021)

Intelligent Editing Result Prediction Service Content

Creative Biogene's Intelligent Editing Result Prediction Service uses cutting edge deep learning and genomic data analysis to handle gene editing difficulties. To evaluate off-target effects, we utilize recursive convolutional networks (R-CRISPR), which encompass mismatches, insertions, and deletions in a complete manner. The sophisticated feature extraction in our model guarantees optimal information retention together with enhanced prediction stability and accuracy. Our techniques, which are supported by a wealth of scientific data, save money and time during experiments while providing effective and dependable results. The specific content includes:

Edit Effect Prediction

Use deep learning models and large-scale genomic data to predict the editing effects of gene editing systems such as CRISPR-Cas9, Cas12, and Cas13, including precise editing of target gene sites and possible off-target effects.

Assessment of Off-Target Effects

By analyzing the editing system's target sequence and potential non-specific recognition sites, we evaluate possible off-target effects during the editing process and provide risk assessment and optimization suggestions.

Optimization of Editing Efficiency

Based on prediction results and customer needs, we provide suggestions for optimizing editing efficiency, including guidance on sgRNA design optimization, Cas protein selection, and experimental condition adjustment.

Explanation of Editing Results

In view of the complexity and diversity of editing results, detailed explanations and analysis reports are provided to help customers understand the mechanism of editing effects and possible influencing factors.

Process Details of Our Intelligent Editing Result Prediction Service

1. Data Preparation and Collection

Collect target gene sequences, editing system information and relevant experimental data provided by customers to build data sets for training and validation.

2. Model Training and Verification

Utilize deep learning technology and large-scale genomic data to train the editing effect prediction model, and ensure the accuracy and generalization ability of the model through cross-validation and validation set testing.

3. Edit Effect Prediction

Based on the target gene site and editing system information provided by the customer, the trained model is used to predict the editing effect, including the prediction of precise base editing, insertion, deletion, and other editing types.

4. Assessment of Off-target Effects

Analyze the editing system's targeting sequences and potential non-specific recognition sites, predict possible off-target effects, and provide an assessment of the location and probability of off-target sites.

5. Optimize Editing Efficiency

Based on the editing effect prediction and off-target effect assessment results, we provide suggestions for optimizing editing efficiency, including guidance on sgRNA design optimization, Cas protein selection, and experimental condition adjustment.

6. Results Delivery and Support

Deliver the edited result prediction and analysis reports to customers, and provide further technical support and consulting services to help customers understand and apply the prediction results, and guide subsequent experimental design and decision-making.

Underpinned by copious amounts of scientific data, Creative Biogene’s Cas Protein Characterization and Transformation Service provides dependable and reasonably priced solutions in a number of domains, such as medication development, agricultural biotechnology, and fundamental research. Put your trust in us to help you save time and money by guiding the design and decision-making of your trial.

* For research use only. Not intended for any clinical use.

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