Guide-Guard: Off-Target Predicting in CRISPR Applications

TL;DR

Guide-Guard is a machine learning tool that predicts off-target effects in CRISPR gene editing with 84% accuracy, capable of training on multiple genes simultaneously.

Contribution

This work introduces Guide-Guard, a novel data-driven machine learning approach for predicting CRISPR off-target effects across different genes.

Findings

Achieves 84% prediction accuracy.

Can be trained on multiple genes simultaneously.

Provides a biological and chemical model perspective.

Abstract

With the introduction of cyber-physical genome sequencing and editing technologies, such as CRISPR, researchers can more easily access tools to investigate and create remedies for a variety of topics in genetics and health science (e.g. agriculture and medicine). As the field advances and grows, new concerns present themselves in the ability to predict the off-target behavior. In this work, we explore the underlying biological and chemical model from a data driven perspective. Additionally, we present a machine learning based solution named \textit{Guide-Guard} to predict the behavior of the system given a gRNA in the CRISPR gene-editing process with 84\% accuracy. This solution is able to be trained on multiple different genes at the same time while retaining accuracy.