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Updated: Sep 20, 2025

Using Next Generation Sequencing to Identify Mutations Associated with Repair of a CAS9-induced Double Strand Break Near the CD4 Promoter
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Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework.

Xiuqin Liu1, Shuya Wang1, Dongmei Ai1

  • 1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.

Cells
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

Predicting CRISPR/Cas9 gene editing outcomes is crucial for its applications. A new deep learning model, Apindel, accurately predicts DNA repair outcomes by analyzing sequence features near cleavage sites.

Keywords:
DNA repairattention mechanismdeep learningpositional encoding

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • CRISPR/Cas9 is a programmable nuclease for genome editing.
  • DNA mutations from CRISPR/Cas9 breaks influence biological effects.
  • Repair outcomes are sequence-dependent but current prediction methods are limited.

Purpose of the Study:

  • To develop a deep learning model for predicting CRISPR/Cas9 editing outcomes.
  • To improve the accuracy and detail of mutation prediction.
  • To understand the influence of sequence features on repair outcomes.

Main Methods:

  • Developed Apindel, a deep learning model.
  • Utilized GloVe for automatic sequence feature training.
  • Incorporated Positional Encoding (PE) for location information.
  • Employed BiLSTM and Attention mechanisms for prediction.

Main Results:

  • Predicted 557 DNA repair labels, covering most Cas9 outcomes.
  • Apindel demonstrated superior performance over existing models.
  • Identified varying nucleotide influences based on position relative to cleavage sites.

Conclusions:

  • Apindel offers accurate and detailed prediction of CRISPR/Cas9 editing outcomes.
  • The model advances the understanding of sequence determinants in genome editing.
  • This work supports the clinical application of CRISPR/Cas9 technology.