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Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells
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Generative modeling for RNA splicing predictions and design.

Di Wu1, Natalie Maus1, Anupama Jha2

  • 1Department of Computer and Information Science, School of Engineering, University of Pennsylvania.

Biorxiv : the Preprint Server for Biology
|February 3, 2025
PubMed
Summary
This summary is machine-generated.

We developed TrASPr+BOS, an AI tool using generative models and Bayesian optimization to predict and design RNA for tissue-specific alternative splicing (AS). This method enhances understanding of gene regulation and aids therapeutic development.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Alternative splicing (AS) is vital for tissue-specific gene regulation and implicated in diseases.
  • Splicing defects can lead to various pathologies, highlighting the need for predictive and manipulative tools.
  • Understanding and controlling AS offers potential for novel therapeutic strategies.

Purpose of the Study:

  • To introduce TrASPr+BOS, a novel generative AI model combined with Bayesian Optimization.
  • To predict and design RNA sequences for achieving specific tissue-dependent alternative splicing outcomes.
  • To advance the design of RNA-based therapeutics and understand gene regulation.

Main Methods:

  • Developed TrASPr, a multi-transformer generative AI model capable of handling diverse AS events and generalizing to new conditions.
  • Utilized TrASPr as an oracle to generate labeled data for training a Bayesian Optimization for Splicing (BOS) algorithm.
  • Employed BOS to design RNA sequences for condition-specific splicing modulation.

Main Results:

  • TrASPr+BOS significantly outperformed existing methods in predicting tissue-specific splicing.
  • Achieved up to a 2.4-fold enhancement in tissue-specific AUPRC (Area Under the Precision-Recall Curve).
  • Identified and validated novel tissue-specific splicing variations and regulatory elements using dCas13 technology.

Conclusions:

  • TrASPr+BOS provides a powerful and accurate approach for predicting and designing RNA for tissue-specific alternative splicing.
  • The model successfully captures key tissue-specific regulatory elements, advancing the field of RNA therapeutics.
  • This method offers a versatile tool for researchers investigating gene regulation and developing targeted therapies.