Computational model for predicting CRISPR editing effects

This technology is a machine learning model that can predict post-CRISPR gene expression in human cell types.

Unmet Need: Tool to predict gene expression changes as a function of gene editing in human cells

CRISPR is a technology that enables researchers to edit parts of the genome by removing, adding, or mutating sections of DNA. Although a powerful tool, CRISPR experiments are labor-intensive and often confounded by environmental factors, making probing gene expression effects a difficult process that may not yield scalable results. There is a need for tools and methods that can improve the efficiency and scalability of genetic research using CRISPR.

The Technology: Computational pipeline to predict expression after gene editing

This technology describes a computational pipeline that predicts gene expression data in human cell types. The model is trained from published human single-cell multi-omics data and accurately predicts gene expression in that cell type from chromatin accessibility data. The ability to perform in silico CRISPR enables a more effective strategy for choosing how to edit the genome in both research and therapeutic applications.

Applications:

  • CRISPR-based therapy response prediction
  • In silico perturbation of regulatory elements
  • Non-coding mutation effect evaluation
  • Enhancer-promoter target prediction
  • Transcription factor interaction analysis
  • Generation of databases of gene editing sites

Advantages:

  • Trained with reliable, rich multi-omics data sets
  • Enables in silico CRISPR
  • Data-driven prediction model
  • Cost-effective CRISPR target selection

Lead Inventor:

Raul Rabadan, Ph.D.

Patent Information:

Patent Pending(WO2024178321)

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