Our goal is to harness extensive single-cell and spatial transcriptome datasets to decipher APA/AS patterns and gene expression simultaneously, expanding the scope beyond conventional single-cell expression analysis.

The broader goals of the project include facilitating the exploration of the RNA interactome for researchers and suggesting potential therapeutic interventions for diseases where RNA dysfunction is a significant factor, such as in cancers and genetic disorders.

This project advances the development of innovative machine learning models designed to unearth conserved cis-regulatory RNA structures across vertebrate genomes. This initiative includes the creation of new RNA structure 2D/3D modeling and comparative genomics methods, underpinned by robust neural network architectures.

This project aims to shed light on the role of APA and its regulation by RNA-binding proteins (RBPs) in the retina, with a particular focus on responses to oxidative stress as observed in AMD.

This project aims to investigate the role of APA in cell fate by studying CD8+ T cells that respond to virus infections and how RNA-binding proteins (RBPs) regulate APA in T cell differentiation.

The Whole Cell Model Development project, spearheaded by our ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS), is dedicated to constructing mathematical and machine learning models of entire cells, known as Whole Cell Models (WCM).