ArticlePlant communications2026
PCdb: A comprehensive plant genome-editing database integrating sgRNA efficiency, off-target predictions, and epigenomic landscapes.
Article in Plant communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
CRISPR-Cas-based genome editing has transformed plant biotechnology by enabling precise genomic modifications for crop improvement and functional genomics. The success of these applications hinges on the design of single guide RNAs (sgRNAs) that maximize on-target efficiency while minimizing off-target effects. However, existing resources for sgRNA design and performance evaluation in plants remain fragmented and lack integration with genomic and epigenomic contexts that influence both editing efficacy and specificity. Here, we present PCdb (Plant CRISPR Database; https://gmo.sjtu.edu.cn/pcdb), a comprehensive plant-focused database that integrates experimentally validated sgRNAs, annotated genomic contexts, genome-wide off-target predictions, and multilayer epigenomic annotations. PCdb encompasses 6172 manually curated editing records from 2132 publications, covering 4320 unique sgRNAs and 6 117 424 predicted off-target sites across nine major plant species. Notably, PCdb contextualizes potential editing outcomes-both on target and off target-within the chromatin landscape by incorporating DNA methylation profiles, chromatin accessibility data, and histone modification patterns. The database features an intuitive web interface that supports flexible queries, interactive visualization tools, and comprehensive analytical modules for sgRNA efficiency assessment and off-target analysis. A case study reanalysis of Oryza sativa yield-related genes demonstrates PCdb's ability to generate a detailed performance profile by evaluating both on-target characteristics and off-target risks within their native epigenomic context. Systematic analysis of the database further identifies key sequence and chromatin features that influence editing outcomes, providing novel insights to improve gene-editing efficacy and specificity.
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