Optimization of epigenome-wide CRISPR-CAS9 knockout screen analysis to prioritize cancer therapeutics

Cancer target identification has been expanded by genome-wide, high-throughput CRISPR knockout screens. Using CRISPR-Cas9 knockdown screening, cancer cell survival genes are identified, and new targeted treatments are developed. Genome-scale knockout screens help with the discovery of essential genes that are needed for cancer cell growth. The examination of the screening data produced by this newly developed technology offers several difficulties. A variety of epigenetic modifiers are the focus of the epigenetic knockout library EPIKOL. Five EPIKOL screenings in two separate cell lines were employed in this study (Prostate and triple-negative breast cancer). Variations in sample size, sgRNA knockout efficiency, and the distribution of read counts make it difficult to interpret findings from CRISPR-Cas9 knockout screen data. Since off-target effects can cause drug development to progress in the wrong direction, it is particularly crucial to comprehend what the results of a genetic screen indicate. Therefore, a promising computational algorithm capable of handling various screening library types and read counts is required. There are multiple methods for genome-wide CRISPR screen analysis; however, not all of them are suitable for small-scale screening libraries. False positives appear to be widespread in small-scale library screen analysis. Our objective is to create the most efficient method for screening small-scale CRISPR libraries. To achieve this objective using EPIKOL data, we compared the outcomes of three distinct methods. The implemented algorithms are MAGeCK, CRISPRcleanR, and BAGEL2. This study illustrates that altering the method of normalization or differential expression analysis can enhance the number of hit genes in small-scale libraries. According to the results of the analysis, BAGEL2 discovers more hit genes than other approaches.