ScholarWorks@숙명여자대학교

초록

Transcriptome data from the gene knockout experiment in mouse is widely used to investigate functions of genes and relationship to phenotypes. When a gene is knocked out, it is important to identify which genes are affected by the knockout gene. Existing methods, including differentially expressed gene (DEG) methods, can be used for the analysis. However, existing methods require cutoff values to select candidate genes, which can produce either too many false positives or false negatives. This hurdle can be addressed either by improving the accuracy of gene selection or by providing a method to rank candidate genes effectively, or both. Prioritization of candidate genes should consider the goals or context of the knockout experiment. As of now, there are no tools designed for both selecting and prioritizing genes from the mouse knockout data. Hence, the necessity of a new tool arises.