X-CRISP: domain-adaptable and interpretable CRISPR repair outcome prediction.

ABSTRACT:

Motivation

Controlling the outcomes of CRISPR editing is crucial for the success of gene therapy. Since donor template-based editing is often inefficient, alternative strategies have emerged that leverage mutagenic end-joining repair instead. Existing machine learning models can accurately predict end-joining repair outcomes; however, generalisability beyond the specific cell line used for training remains a challenge, and interpretability is typically limited by suboptimal feature representation and model architecture.

Results

We propose X-CRISP, a flexible and interpretable neural network for predicting repair outcome frequencies based on a minimal set of outcome and sequence features, including microhomologies (MH). Outperforming prior models on detailed and aggregate out

SUBMITTER: Seale C 

PROVIDER: S-EPMC12270252 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature