Genetic Functional Similarity Clustering Using CRISPR-Cas9 Knockout Data

Abstract

Abstract Background: Machine learning models have become a powerful tool in the field of genetics, allowing scientists to make more accurate predictions about the functions of genes using currently available information. Utilizing both pre-existing annotations from previous studies and multiple genome-wide experimental data would provide us with the potential to construct a more comprehensive model about the functional similarity between genes. Results: In this paper, we used knockout phenotype information obtained from CRISPR-cas9 knockout experiments performed under various conditions and using various cells to improve gene functional similarity prediction. We applied Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and Agglomerative Hierarchical Clustering algorithms to find functionally linked gene groups from knockout data. Subsequent gene enrichment analysis revealed that gene groups defined with knockout data could be associated with specific biological functionality with a high degree of statistical significance. Furthermore, we were able to identify possible functional similarities between an undescribed gene and previously researched genes by using HDBSCAN labels. As a case study, we manually investigated KCNA1/SCN9A pair, which showed highly similar HDBSCAN label profiles, and identified that they were both associated with Oncogene-Induced Senescence (OIS), information that was not found in available databases. Conclusion: We found that previously unaddressed functional similarities between genes could be identified from genome-wide CRISPR-Cas9 phenotype datasets. This approach might help to identify novel biomarkers or potential drug targets for diseases with few therapeutic options.