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Bioinformatic Tool Development for Anti-CRISPR Research
CRISPR-Cas is an anti-viral mechanism of prokaryotes that has been widely adopted for the field of gene editing. First discovered in 2013, anti-CRISPRs (Acrs) have been recognized with huge potential in the development of more controllable genome editing applications. Encoded by (pro)phages/(pro)viruses, Acr proteins can inhibit their host's CRISPR-Cas systems. However, with only 98 Acr proteins experimentally characterized so far, the filed is in desperate need of novel computational tools and resources to assist new Acr discovery. In addition to Acrs, Acr-associated (Aca) proteins have recently been identified as Acr transcriptional regulators. This feature provides Acas with the potential of offering extra control to gene editing technologies. As genes encoding Acr and Aca proteins often colocalize to form Acr-Aca operons, we constructed the first ever online Acr-Aca operon database, AcrDB. The database focuses on providing user-friendly access to our own computationally predicted operons with associated genomic context. Being vastly understudied, no bioinformatic resource exist for Aca research. Hence, we developed the first automated genome wide Aca screening tool, AcaFinder. AcaFinder implements the guilt-by-association idea and the Hidden Markov Models (HMMs) of known Acas into one software package. Applying AcaFinder in screening prokaryotic and gut phage genomes, we discovered a complex Acr-Aca operonic colocalization network between different families of Acrs and Acas. Recent studies revealed the importance of the genomic context of known Acr genes. However, none of the current tools have fully considered this feature. We have developed a new computer program AOminer to facilitate the improved discovery of new Acrs by fully exploiting the genomic context of known Acr genes and their homologs using a two-state Hidden Markov Model (HMM). AOminer allows automated mining for potential Acr operons (AOs) from query genomes/operons and outperformed all existing Acr prediction tools. In conclusion, we present three novel bioinformatic resources, aimed to provide researchers with high quality Acr/Aca predictions that can be further processed with experimental characterization and validation.
Yang, Bowen, "Bioinformatic Tool Development for Anti-CRISPR Research" (2023). ETD collection for University of Nebraska - Lincoln. AAI30419787.