Computational Tools

HiC-ECC

We developed HiC-ECC (HiC Enhance, Compare, and Call), a comprehensive and modular workflow for analyzing 3D genome organization from Hi-C data. HiC-ECC integrates state-of-the-art tools to enhance low-resolution Hi-C maps using deep learning, systematically compare chromatin interactions across tissues, and identify key 3D genome structures including topologically associating domains (TADs) and statistically significant chromatin loops. By offering a flexible, interchangeable, and visualization-ready framework, HiC-ECC can be used to extract biologically meaningful features from complex Hi-C datasets and advances our ability to link 3D genome architecture with functional genomics.

ChromXplorer

In collaboration with Dr. Ayush Raman, Broad Institute of MIT and Harvard

In recent work, we developed ChromXplorer, an algorithm designed to compare chromatin states across different groups of samples (e.g., normal vs. cancer). While individual histone marks can provide insights into sample differences, a more comprehensive approach—integrating multiple histone marks to identify combinatorial chromatin states using ChromHMM—offers a deeper and more accurate reflection of each sample's underlying biology. ChromXplorer enables researchers to systematically compare these chromatin states across groups, enhancing our understanding of epigenomic variations and their roles in disease.

Methylation-eQTL

In collaboration with Prof. Jeff Morris, University of Pennsylvania

Methylation-eQTL provides an interactive platform to explore methylation-expression quantitative trait loci (methyl-eQTLs) for genes across colorectal, breast, and pancreatic cancer types. This tool uses a novel sequential penalized regression approach to identify CpG sites that best explain gene expression variability, offering tissue- and gene-specific insights into methylation-driven regulation. Researchers can visualize important CpG loci and their association with gene expression, allowing them to understand methylation's role in gene regulation. The app also includes features for viewing gene-level methylation summaries and pathway-specific heatmaps, making it a valuable resource for integrative genomic analyses in cancer research​