Ruoxi Yuan, PhD

At the Genomics Center

Designs, develops, and maintains robust and reproducible pipelines for next-generation sequencing (NGS) data, including RNA-seq, ATAC-seq, ChIP-seq, and spatial transcriptomics.

Manages large-scale biological databases; performs data wrangling, integration, and curation for downstream statistical and genomic analyses. Works closely with wet-lab scientists to understand experimental goals and ensure proper analytical approaches; communicates findings clearly to both technical and non-technical audiences. Contributes to scientific manuscripts, presentations, and grant proposals by providing high-quality figures, statistical results, and biological interpretations.

Research Interests

The regulation of gene expression is fundamental to cellular function and is governed by epigenetic mechanisms that control access to DNA and its transcription. These mechanisms include histone modifications, DNA accessibility, and spatial organization of genomic elements within the nucleus. Dysregulation of these processes is implicated in complex diseases, such as autoimmune disorders and cancers, where genetic variants often lie in regulatory regions rather than coding sequences. Understanding these epigenetic landscapes at high resolution—down to the single-cell and spatial level—is critical for unraveling disease mechanisms and identifying therapeutic targets.

My research centers on analyzing advanced epigenomic techniques, such as CUT&RUN, ChIP-seq, ATAC-seq, and their corresponding single-cell methods, to precisely map histone modifications, transcription factor binding, and chromatin accessibility. CUT&RUN and ChIP-seq enable detailed profiling of protein-DNA interactions and histone marks, while ATAC-seq reveals chromatin accessibility variations across different conditions, uncovering cellular heterogeneity in healthy and diseased states. Additionally, I explore spatial transcriptomics to investigate how gene expressions are organized within the three-dimensional context of tissues. By integrating these approaches, my work aims to elucidate the epigenetic and spatial underpinnings of gene regulation in complex diseases, providing insights into their molecular mechanisms and potential interventions.

Optimizing Data Visualization

Next-generation sequencing (NGS) technologies, including RNA-seq, CUT&RUN, ChIP-seq, ATAC-seq, and spatial transcriptomics, generate vast datasets that provide critical insights into gene expression, epigenetic regulation, and spatial organization of genomic activity. However, the complexity and volume of these datasets pose significant challenges for researchers in interpreting and exploring their results. My work also focuses on optimizing the visualization of NGS data, with a primary emphasis on developing interactive tools using the Shiny platform. I am dedicated to enhancing the end-user experience for researchers exploring both public and private datasets, empowering them to uncover novel insights into gene regulation, epigenetic mechanisms, and spatial transcriptomic profiles in complex biological systems.