Solution to design and evaluate sgRNA candidates for CRISPR/Cas9 based screens

Company Information

With the advent of CRISPR/Cas9 technology revolutionizing genomic editing, the need for precise, efficient sgRNA (single-guide RNA) selection has become paramount. A client sought ProCogia’s expertise to develop a solution that optimizes sgRNA candidate design and evaluation, leveraging the vast potential of bioinformatics, machine learning, and multi-omics data analysis. This endeavor aimed to enhance the efficacy and efficiency of CRISPR/Cas9 screens, a critical component in gene editing research and applications.

The Challenge

The primary challenge was to create a tool that not only streamlined the sgRNA design process but also incorporated predictive analytics to evaluate sgRNA efficacy using genetic and epigenetic information. The complexity of integrating multi-omics data and the need for a user-friendly interface presented significant hurdles. Moreover, the solution required adherence to Bioconductor guidelines to ensure scientific rigor and community accessibility.

Procogia’s Approach

The solution was delivered by our Bioinformatics team. We used our expertise in Bioinformatics, genome-wide screening, R, Python, and machine learning. We evaluated existing approaches and advised on and evaluated methodology using publicly available and proprietary multi-omics data to ensure the results were scientifically sound.

We developed an R package that handles user input and output while leveraging automated conda environment calls to seamlessly handle operations in Python modules. Calls to Python were implemented using reticulate and conda environments were managed using basilisk.

We built a machine learning model based on previously published scientific literature to predict sgRNA efficiency and efficacy. The trained model utilizes genetics and epigenetics information obtained from multi-omics datasets to make these predictions.

We utilized ground truth data to develop, train, and test the machine learning model for predictive scoring of sgRNA candidates.

The Results

We delivered an R package that follows Bioconductor guidelines and best practices.

The complete package, including trained model and Python components, were delivered in a portable, easy-to-use R package.

Due to the novelty of the tool, we are working with the client to prepare the tool for scientific publication and Bioconductor submission to make it available to the wider community.

Services Used


We deliver scientific results that drive clinical and translational research decisions. Our Bioinformatics team has extensive experience designing, optimizing, executing and analyzing pre-clinical and clinical research projects using next-generation sequencing technologies.

Data Consultancy

We provide data consultancy to organizations to optimize your investment in people, processes, and technology. This is typically through data strategy engagements, roadmaps, transformations, and independent technology advice.

Data Science

We use open source technology to leverage the full potential of your data. Predictive and prescriptive results are actioned using AI and Machine Learning (ML).


ProCogia’s development of a comprehensive solution for sgRNA candidate design and evaluation represents a significant advancement in CRISPR/Cas9 technology application. By combining bioinformatics expertise with machine learning and multi-omics data analysis, ProCogia has provided researchers with a powerful tool to enhance gene editing precision and efficiency. This project not only underscores ProCogia’s role in pushing the boundaries of genetic research but also its dedication to contributing valuable resources to the scientific community, marking a pivotal step forward in the field of bioinformatics and genomic editing.

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