PT  - JOURNAL ARTICLE
AU  - Wu, Di
AU  - Maus, Natalie
AU  - Jha, Anupama
AU  - Yang, Kevin
AU  - Wales-McGrath, Benjamin D.
AU  - Jewell, San
AU  - Tangiyan, Anna
AU  - Choi, Peter
AU  - Gardner, Jacob R.
AU  - Barash, Yoseph
TI  - Generative modeling for RNA splicing predictions and design
AID  - 10.1101/2025.01.20.633986
DP  - 2025 Jan 01
TA  - bioRxiv
PG  - 2025.01.20.633986
4099  - http://biorxiv.org/content/early/2025/01/24/2025.01.20.633986.short
4100  - http://biorxiv.org/content/early/2025/01/24/2025.01.20.633986.full
AB  - Alternative splicing (AS) of pre-mRNA plays a crucial role in tissue-specific gene regulation, with disease implications due to splicing defects. Predicting and manipulating AS can therefore uncover new regulatory mechanisms and aid in therapeutics design. We introduce TrASPr+BOS, a generative AI model with Bayesian Optimization for predicting and designing RNA for tissue-specific splicing outcomes. TrASPr is a multi-transformer model that can handle different types of AS events and generalize to unseen cellular conditions. It then serves as an oracle, generating labeled data to train a Bayesian Optimization for Splicing (BOS) algorithm to design RNA for condition-specific splicing outcomes. We show TrASPr+BOS outperforms existing methods, enhancing tissue-specific AUPRC by up to 2.4 fold and capturing tissue-specific regulatory elements. We validate hundreds of predicted novel tissue-specific splicing variations and confirm new regulatory elements using dCas13. We envision TrASPr+BOS as a light yet accurate method researchers can probe or adopt for specific tasks.Competing Interest StatementThe authors have declared no competing interest.