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Related Concept Videos

Homologous Recombination02:31

Homologous Recombination

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The basic reaction of homologous recombination (HR) involves two chromatids that contain DNA sequences sharing a significant stretch of identity. One of these sequences uses a strand from another as a template to synthesize DNA in an enzyme-catalyzed reaction. The final product is a novel amalgamation of the two substrates. To ensure an accurate recombination of sequences, HR is restricted to the S and G2 phases of the cell cycle. At these stages, the DNA has been replicated already and the...
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CRISPR01:59

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Genome editing technologies allow scientists to modify an organism’s DNA via the addition, removal, or rearrangement of genetic material at specific genomic locations. These types of techniques could potentially be used to cure genetic disorders such as hemophilia and sickle cell anemia. One popular and widely used DNA-editing research tool that could lead to safe and effective cures for genetic disorders is the CRISPR-Cas9 system. CRISPR-Cas9 stands for Clustered Regularly Interspaced...
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Mismatch Repair01:20

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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Articles linked to this work by shared authors, journal, and citation graph.

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Same author

MUSICiAn: genome-wide identification of genes involved in DNA repair via control-free mutational spectra analysis.

NAR genomics and bioinformatics·2026
Same author

Signatures in CRISPR Mutational Spectra Reveal Role and Interplay of Genes in DNA Repair.

bioRxiv : the preprint server for biology·2025
Same author

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

Bioinformatics advances·2025
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MUSICiAn: Genome-wide Identification of Genes Involved in DNA Repair via Control-Free Mutational Spectra Analysis.

bioRxiv : the preprint server for biology·2025
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SNMF: Integrated Learning of Mutational Signatures and Prediction of DNA Repair Deficiencies.

bioRxiv : the preprint server for biology·2024
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Author Correction: Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP).

Nature cell biology·2024

Related Experiment Video

Updated: May 27, 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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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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X-CRISP: Domain-Adaptable and Interpretable CRISPR Repair Outcome Prediction.

Colm Seale1,2, Joana P Gonçalves1

  • 1Pattern Recognition & Bioinformatics, Department of Intelligent Systems, EEMCS Faculty, Delft University of Technology, 2628 XE Delft, The Netherlands.

Biorxiv : the Preprint Server for Biology
|February 20, 2025
PubMed
Summary

X-CRISP, a new machine learning model, accurately predicts CRISPR gene editing outcomes using minimal sequence features. This interpretable tool enhances CRISPR gene therapy by improving prediction accuracy and generalizability across different cell types.

Keywords:
CRISPRDNA repairexplainabilityinterpretabilitymachine learningtransfer learning

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CIRCLE-Seq for Interrogation of Off-Target Gene Editing
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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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A Standard Methodology to Examine On-site Mutagenicity As a Function of Point Mutation Repair Catalyzed by CRISPR/Cas9 and SsODN in Human Cells
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CIRCLE-Seq for Interrogation of Off-Target Gene Editing
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CIRCLE-Seq for Interrogation of Off-Target Gene Editing

Published on: November 1, 2024

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • CRISPR gene editing outcomes are critical for gene therapy success.
  • Donor template-based editing is often inefficient, leading to reliance on mutagenic end-joining repair.
  • Existing machine learning models struggle with generalizability and interpretability for predicting end-joining repair outcomes.

Purpose of the Study:

  • To develop a flexible and interpretable neural network, X-CRISP, for predicting CRISPR editing repair outcome frequencies.
  • To improve the generalizability and interpretability of machine learning models for CRISPR editing predictions.
  • To leverage transfer learning for adapting models to new cell lines and experimental conditions with reduced data requirements.

Main Methods:

  • Developed X-CRISP, a neural network utilizing minimal outcome and sequence features, including microhomologies (MH).
  • Evaluated X-CRISP's performance against prior models on detailed and aggregate outcome predictions.
  • Employed transfer learning by pre-training X-CRISP on wild-type mouse embryonic stem cell (mESC) data and adapting it to human cell lines (K562, HAP1, U2OS) and modified mESC lines.

Main Results:

  • X-CRISP outperformed existing models in predicting both detailed and aggregate CRISPR editing repair outcomes.
  • The model prioritized microhomology (MH) location over sequence properties like GC content for predicting deletion outcomes.
  • Adapted X-CRISP models showed significant improvement over direct training on target data, even with as few as 50 samples.

Conclusions:

  • X-CRISP offers a flexible, interpretable, and accurate approach to predicting CRISPR editing outcomes.
  • Transfer learning with X-CRISP enables efficient model adaptation to new cell types, significantly reducing data needs.
  • This strategy holds promise for advancing CRISPR gene therapy by improving the control and predictability of gene editing processes.