Workflow of the system for CRISPR Outcome and Risk Evaluation (SCORE)

It is unclear how CRISPR editing outcomes vary across the genome and
whether undesirable events such as structural variants (SVs) are
predictable or preventable. Here, we describe a computational workflow to
process whole-genome sequencing (WGS) data generated for multiplexed
CRISPR/Cas genome editing experiments. The workflow characterizes and
classifies diverse editing outcomes resulting from CRISPR and trains a
predictive model based on a machine-learning-based framework termed SCORE
(System for CRISPR Outcome and Risk Evaluation).