Predicting and visualizing features of CRISPR-Cas systems

Pervasive application of CRISPR-Cas systems in genome editing has prompted an increase in both interest and necessity to further elucidate existing systems as well as discover putative novel systems. The ubiquity and power of current computational platforms have made in silico approaches to CRISPR-Cas identification and characterization accessible to a wider audience and increasingly amenable for processing extensive data sets. Here, we describe in silico methods for predicting and visualizing notable features of CRISPR-Cas systems, including Cas domain determination, CRISPR array visualization, and inference of the protospacer-adjacent motif. The efficiency of these tools enables rapid exploration of CRISPR-Cas diversity across prokaryotic genomes and supports scalable analysis of large genomic data sets.