Generative modeling for RNA splicing code predictions and design
Keywords: RNA Splicing, Computational Biology, Transformers, Deep Learning, Bayesian Optimization, RNA Design
TL;DR: we construct a transformer based model, TrASPr, which predicts splicing in a tissue specific manner and combine it with a generative bayesian optimization algorithm to design RNA sequence for a tissue specific outcome.
Abstract: Alternative splicing (AS) of pre-mRNA splicing is a highly regulated process with diverse phenotypic effects ranging from changes in AS across tissues to numerous diseases. The ability to predict or manipulate AS has therefore been a long time goal in the RNA field with applications ranging from identifying novel regulatory mechanisms to designing therapeutic targets. Here we take advantage of generative model architectures to address both the prediction and design of RNA splicing condition-specific outcome. First, we construct a predictive model, TrASPr, which combines multiple transformers along with side information to predict splicing in a tissue specific manner. Then, we exploit TrASPr as on Oracle to produce labeled data for a Bayesian Optimization (BO) algorithm with a costume loss function for RNA splicing outcome design. We demonstrate TrASPr significantly outperforms recently published models and that it can identify relevant regulatory features which are also captured by the BO generative process.
Submission Number: 15139
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