PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins


Anti-CRISPRs are widespread amongst bacteriophage and promote bacteriophage infection by inactivating the bacterial host’s CRISPR-Cas defence system. Identifying and characterizing anti-CRISPR proteins opens an avenue to explore and control CRISPR-Cas machineries for the development of new CRISPR-Cas based biotechnological and therapeutic tools. Past studies have identified anti-CRISPRs in several model phage genomes, but a challenge exists to comprehensively screen for anti-CRISPRs accurately and efficiently from genome and metagenome sequence data. Here, we have developed an ensemble learning based predictor, PaCRISPR, to accurately identify anti-CRISPRs from protein datasets derived from genome and metagenome sequencing projects. PaCRISPR employs different types of feature recognition united within an ensemble framework. Extensive cross-validation and independent tests show that PaCRISPR achieves a significantly more accurate performance compared with homology-based baseline predictors and an existing toolkit. The performance of PaCRISPR was further validated in discovering anti-CRISPRs that were not part of the training for PaCRISPR, but which were recently demonstrated to function as anti-CRISPRs for phage infections. Data visualization on anti-CRISPR relationships, highlighting sequence similarity and phylogenetic considerations, is part of the output from the PaCRISPR toolkit, which is freely available at

Nucleic acids research
Jiawei Wang
Jiawei Wang
Marie Curie Fellow
EMBO Non-Stipendiary Fellow

My research interests include computational biology, computer science, machine learning and single cell omics.