CAFRI‐Rice: CRISPR applicable functional redundancy inspector to accelerate functional genomics in rice

SUMMARY Rice (Oryza sativa L.) is a staple crop with agricultural traits that have been intensively investigated. However, despite the variety of mutant population and multi‐omics data that have been generated, rice functional genomic research has been bottlenecked due to the functional redundancy in the genome. This phenomenon has masked the phenotypes of knockout mutants by functional compensation and redundancy. Here, we present an intuitive tool, CRISPR applicable functional redundancy inspector to accelerate functional genomics in rice (CAFRI‐Rice; cafri‐rice.khu.ac.kr). To create this tool, we generated a phylogenetic heatmap that can estimate the similarity between protein sequences and expression patterns, based on 2,617 phylogenetic trees and eight tissue RNA‐sequencing datasets. In this study, 33,483 genes were sorted into 2,617 families, and about 24,980 genes were tested for functional redundancy using a phylogenetic heatmap approach. It was predicted that 7,075 genes would have functional redundancy, according to the threshold value validated by an analysis of 111 known genes functionally characterized using knockout mutants and 5,170 duplicated genes. In addition, our analysis demonstrated that an anther/pollen‐preferred gene cluster has more functional redundancy than other clusters. Finally, we showed the usefulness of the CAFRI‐Rice‐based approach by overcoming the functional redundancy between two root‐preferred genes via loss‐of‐function analyses as well as confirming the functional dominancy of three genes through a literature search. This CAFRI‐Rice‐based target selection for CRISPR/Cas9‐mediated mutagenesis will not only accelerate functional genomic studies in rice but can also be straightforwardly expanded to other plant species. Significance Statement Although a huge number of gene‐indexed mutant population and multi‐omics data are available, functional genomic studies in rice, which is a representative crop model, are still not making rapid progress. The main reason for this is the redundancy of gene function in the genome; to overcome this limitation, the best candidate gene(s) for gene editing technology is (are) proposed through the CAFRI‐Rice tool, which combines phylogenetic tree and transcriptome analyses.