Research

The lab is interested in regulatory genomics to understand the fundamental mechanisms of gene expression regulation. We focus on enhancer regulation and a systems-level understanding of enhancer architectures and functions in various contexts. Transcription factor (TF) binding and remodeling of chromatin landscape are essential processes in gene transcriptional regulations. Despite the exponentially increasing genome-wide mapping data for TF and chromatin, their architecture-dependent functions and specificity are still elusive, and more sophisticated computational methods are required to answer these questions. To this end, we develop statistical and machine learning-based methods integrating various multi-omics data 1) to delineate heterogeneous architectures of TF binding events and chromatin landscape in high resolution and 2) to predict architecture-dependent functions. Ultimately, we aim to predict regulatory functions of genomics regions in a biological context-dependent manner and their perturbations upon genetic variations. Example data types that we deal with include ChIP-seq, ChIP-exo, CUT&RUN, GRO-seq, RNA-seq, ATAC-seq, Hi-C, and single-cell data. (See full publications in Google Scholar). Given the fundamental nature of the research, we broadly collaborate with multiple wet-laboratories.

High-resolution architecture of regulatory elements

Transcription factors (TF) are key players in gene transcription regulation. TFs exert their functions by binding to genomic regulatory regions such as enhancers and promoters. However, their functional and architectural specificity is not well understood. We develop machine learning-based computational tools to investigate the high-resolution architectures of TF-binding and chromatin landscape utilizing ChIP-exo and CUT&RUN and delineate intrinsically heterogeneous context-dependent functions.

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Regulatory genomics of enhancer RNA

Enhancer RNA (eRNA) is a small non-coding RNA transcribed from enhancer regions. Although the functions are still under debate, the unique bidirectional transcription signature allows us to identify active enhancers and monitor their activities directly. We utilize nascent RNA detection techniques such as GRO-seq and csRNA-seq for direct monitoring of enhancer regulations, predicting key TFs responsible for the regulation, thereby building TF-gene transcriptional networks.

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Single-cell biology

Recent advances in single-cell techniques enabled us to investigate comprehensive cellular heterogeneity and repertoire in various tissues. We use single-cell techniques to investigate pathophysiology and developmental biology.

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Regulatory networks of adipose tissues

Adipose tissue is not only a storage system of excess energy but also a very important endocrine organ and a therapeutic target. There is more than one type of adipose depot, which differs by morphology and function and displays plasticity to change to a different type. We use multi-omics data to identify previously unknown key players of adipocytes and uncover their regulatory mechanisms.