CALITAS: A CRISPR-Cas-aware ALigner for In silico off-TArget Search

Abstract

We describe CALITAS, a CRISPR-Cas-aware aligner and integrated off-target search algorithm. CALITAS uses a modified and CRISPR-tuned version of the Needleman-Wunsch algorithm. It supports an unlimited number of mismatches and gaps and allows protospacer adjacent motif (PAM) mismatches or PAMless searches. CALITAS also includes an exhaustive search routine to scan genomes and genome variants provided with a standard Variant Call Format file. By default, CALITAS returns a single best alignment for a given off-target site, which is a significant improvement compared to other off-target algorithms, and it enables off-targets to be referenced directly using alignment coordinates. We validate and compare CALITAS using a selected set of target sites, as well as experimentally derived specificity data sets. In summary, CALITAS is a new tool for precise and relevant alignments and identification of candidate off-target sites across a genome. We believe it is the state of the art for CRISPR-Cas specificity assessments.