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Fig. 1 Examination of the relationship between predicted scores from online tools and phenotype penetrance for each gRNA. Four genes were selected based on the knockout phenotype, which includes (A) absence of notochord (arrow) in noto knockout at 2 dpf, (B) missing eyes (arrowheads) in rx3 knockout at 2 dpf, (C) lack of pigmentation in slc45a2 knockout at 3 dpf, and (D) pericardial edema (arrow and closed circle) in rtf1 knockout at 2 dpf. The gRNAs (indicated by numbered triangles) were designed across gene exons (gray boxes) and the corresponding functional domain (s) encoded by exons (yellow boxes) for each gene were displayed in colored boxes below. Brown arrows indicate the forward (qF) and reverse (qR) primer for RT-qPCR. The introns were removed to simplify the schematics. The injected animals were first categorized by morphological phenotype as shown in each figure in the right panel and then calculated as a percentage of the total. The editing efficiency of each gRNA was analyzed using TIDE and ICE. (E) Grade 0 indicates that over 90% of injected animals for a gRNA exhibit no effect on phenotype; Grade 1 indicates that less than 10% fall into the strong category; Grade 2 indicates that between 10% and less than 25% are in the strong category; Grade 3 indicates that between 25% and less than 50% belong to the strong category; Grade 4 indicates that between 50% and less than 80% are categorized in the strong category; Grade 5 indicates that over 80% are classified in the strong category. (F) Quantification of phenotype penetrance for gRNAs targeting outside (OD) and inside (ID) of the functional domain. Each dot represents one gRNA. (G) A scatter plot was used to visualize the distribution of predicted scores for a gRNA generated from different online tools. Each dot represents one gRNA, and the mean value ± standard deviation (SD) of each group is placed at the bottom of the respective bar in the figure..