Advanced pipeline for CRISPR/Cas9 offtargets detection in Guide-seq and related integration-based assays

BackgroundThe advent of CRISPR-Cas9 genome editing has brought about a paradigm shift in molecular biology and gene therapy. However, the persistent challenge of off-target effects continues to hinder its therapeutic applications. Unintended genomic alterations can lead to significant genomic damage, thereby compromising the safety and efficacy of CRISPR-based therapies. Although in-silico prediction tools have made substantial progress, they are not sufficient for capturing the complexity of genomic alterations and experimental validation remains crucial for accurate identification and quantification of off-target effects. In this context, Genome-wide Unbiased Identification of Double-strand breaks Enabled by Sequencing (GUIDE-Seq) has emerged as a gold standard method for the experimental detection of off-target sites and assessment of their prevalence by introducing short double-stranded oligonucleotides (dsODNs) at the break sites created by the nuclease. The bioinformatic analysis of GUIDE-Seq data plays a pivotal yet challenging role in accurately mapping and interpreting editing sites and current pipelines suffer limitations we aim to address in this work. ResultsIn this study, we present a rapid and versatile single-command pipeline designed for the comprehensive analysis of GuideSeq and similar techniques of sequencing. Our pipeline is capable of simultaneously processing multiplexed libraries from different organisms, PCR orientations, and Cas with different PAM specificities in a single run, all based on user-specified sample information. To ensure reproducibility, the pipeline operates within a closed environment and incorporates a suite of well-established bioinformatics tools. Key novel features include the ability to manage bulges in gDNA/gRNA interaction and multi-hit reads, and a built-in tool for off-target site prediction. The pipeline generates a detailed report that consolidates quality control metrics and provides a curated list of off-target candidates along with their corresponding gRNA alignments. ConclusionsOur pipeline has been tested and successfully applied to analyze samples under a variety of experimental conditions, including different source organisms, PAM motifs, dsODN sequences and PCR orientations. The robustness and flexibility of our pipeline make it a valuable tool for researchers in the field of genome editing. The source code and comprehensive documentation are freely accessible on our GitHub repository: https://github.com/gcorre/GNT_GuideSeq.