Joy S. Xiang

Genome-Wide Interrogation of SARS-CoV-2 RNA-Protein Interactions Uncovers Hidden Regulatory Sites

This study maps SARS-CoV-2 protein-RNA interactions using eCLIP, revealing new regulatory sites for viral replication and translation.

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNASeek is a generative foundation model for RNA sequence design and functional prediction, using reinforcement learning to optimize RNA properties like stability and cleavage activity.

A conserved long-range RNA interaction in SARS-CoV-2 recruits ADAR1 to enhance virus proliferation

RNA structure directs RNA partitioning and is actively disrupted inside stress granules to enable cellular recovery

The paper investigates how RNA structure influences stress granule formation and recovery, showing that single-stranded RNA is preferentially included in stress granules and that RNA-binding proteins and helicases regulate this process.

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence

HydRA predicts RNA-binding capacity using protein interaction and sequence data, identifying novel RNA-binding domains with high specificity and sensitivity.

Discovery and functional interrogation of SARS-CoV-2 protein-RNA interactions

This study identifies SARS-CoV-2 protein-RNA interactions in infected cells, revealing roles in viral processes and potential antiviral targets.