SpacerScope: binary-vectorized, genome-wide off-target profiling for RNA-guided nucleases without prior candidate-site bias

作者：Qu Y, Wang Y, Wang Y, Tang H, Chen Q · 发表于：Briefings in bioinformatics · 年份：2026 · DOI：10.1093/bib/bbag478

The precision of CRISPR/Cas systems is fundamental to their application in plant and animal biotechnology. However, comprehensive sequence-based off-target candidate discovery remains a computational bottleneck, particularly in large and complex genomes. Here we developed SpacerScope, an off-target candidate discovery framework that enables unbiased, genome-wide discovery by leveraging binary vectorization, bitwise filtering, and right-end-anchored alignment. Benchmarking against human CIRCLE-seq data demonstrated that SpacerScope recovered 100% of validated off-target sites (6142/6142), matching the sensitivity of exhaustive algorithms. Crucially, SpacerScope achieved this maximum candidate recovery while substantially reducing computational overhead. In large-genome evaluations, SpacerScope maintained low peak memory usage of 2.20 GiB and achieved substantial runtime improvements over indel-aware comparator tools, including more than 50-fold speedup relative to Cas-OFFinder 3 (544 s versus 29 185 s). Furthermore, comparative analyses in polyploid species, such as the octoploid strawberry, revealed that SpacerScope identified larger sequence-compatible candidate burdens than standard web-based design platforms. Our results establish SpacerScope as a high-speed framework for sequence-based genome-wide off-target candidate discovery across diverse and highly repetitive genomic landscapes. The source code and program was publicly available at https://github.com/charlesqu666/Spa...