A flexible pipeline for Off-Targets nomination in GUIDE-Seq derived experiments

The 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.