DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning

TL;DR

DeepFM-Crispr is a deep learning model that accurately predicts the efficiency and off-target effects of Cas13d-based gene editing by leveraging large language models and transformer architecture.

Contribution

It introduces a novel deep learning framework that integrates evolutionary and structural data for improved CRISPR-Cas13d on-target and off-target effect prediction.

Findings

Outperforms traditional prediction models in accuracy

Uses large language models for comprehensive RNA representation

Provides reliable predictions of Cas13d efficacy and off-target effects

Abstract

Since the advent of CRISPR-Cas9, a groundbreaking gene-editing technology that enables precise genomic modifications via a short RNA guide sequence, there has been a marked increase in the accessibility and application of this technology across various fields. The success of CRISPR-Cas9 has spurred further investment and led to the discovery of additional CRISPR systems, including CRISPR-Cas13. Distinct from Cas9, which targets DNA, Cas13 targets RNA, offering unique advantages for gene modulation. We focus on Cas13d, a variant known for its collateral activity where it non-specifically cleaves adjacent RNA molecules upon activation, a feature critical to its function. We introduce DeepFM-Crispr, a novel deep learning model developed to predict the on-target efficiency and evaluate the off-target effects of Cas13d. This model harnesses a large language model to generate comprehensive representations rich in evolutionary and structural data, thereby enhancing predictions of RNA secondary structures and overall sgRNA efficacy. A transformer-based architecture processes these inputs to produce a predictive efficacy score. Comparative experiments show that DeepFM-Crispr not only surpasses traditional models but also outperforms recent state-of-the-art deep learning methods in terms of prediction accuracy and reliability.