My laboratory’s research interests span a wide range of computational genomics activities including: i) developing low-level data processing and analysis pipelines for next-generation sequencing (NGS) datasets; ii) building data analysis, visualization and integration platforms to study epigenetic datasets in integration with other functional data (e.g., motifs; pathways; gene modules) and other NGS datasets (e.g., RNA-seq, ChIA-PET); and iii) implementing novel machine-learning applications to extract knowledge from epigenetic assays in relation to dynamic gene regulation. Specifically, I categorize my lab’s research activities into three major areas, each of which is critical for analyzing and interpreting human epigenome samples: i) putative epigenetic biomarker identification and interpretation; ii) three-dimensional chromatin interactions to identify targets of critical sites; iii) predictive epigenomic models for utilizing epigenetic patterns in human conditions.
Epigenetic biomarker identification
Genomewide association studies (GWAS) identified that a majority of the single nucleotide polymorphisms (SNPs) reside in non-coding regions of the genome. Recent epigenomic technologies such as open chromatin assays (ATAC-seq) provide an unprecedented opportunity to identify disease-causing, non-coding regulatory elements for complex human diseases. Moreover chromatin interactions assays (e.g., ChIA-PET) enable a refined interpretation of these regulatory elements. We are actively collaborating with the Stitzel, Palucka and Banchereau labs at The Jackson Laboratory for Genomic Medicine to uncover epigenetic remodeling associated with human diseases including type 2 diabetes (T2D), auto-immune diseases and cancer.
The immune system and the repertoire of immune cells play an important role in various aspects of human health including auto-immune diseases, cancer, aging and infections. Advancements in next-generation technologies enabled genomic and epigenomic profiles from small numbers of cells, and therefore the study of immunological mechanisms under diverse conditions. We actively collaborate with the Banchereau and Palucka labs to study human immune system via generation and analysis of diverse immunogenomics data.
Three-dimensional chromatin analyses
One major challenge in interpreting the role that regulatory elements might play in human diseases is the complexity associated with the three-dimensional (3D) structure of the human genome. Genomic technologies for mapping 3D chromatin structure, i.e., ChIA-PET and HiC, have identified many regulatory elements separated by a large base-pair distance on the linear genome map that are actually in close physical proximity/contact as a result of the chromatin looping. The data generated by these technologies is the starting point from which we can begin to infer distal regulatory interactions and their system-level effects. In my lab, we build computational tools to analyze chromatin interaction datasets and integrate it with epigenetic datasets.