3D Genomics

Developing advanced statistical methods and computational tools for analyzing 3D genome structure data, with applications in understanding gene regulation mechanisms and disease-associated genetic variants.

Deep generative modeling framework for normalization and de-noising of single-cell Hi-C data

R package for extracting three-dimensional chromatin interactions at the gene level and integrating single-cell 3D genomics with other modalities

Fast and efficient band normalization method for single-cell Hi-C data analysis

High-fidelity Hi-C data simulation pipeline using Fragment Interactions Empirical Estimation method

Python pipeline for simulating differential chromatin interactions with data-driven or user-specified spike-ins

Robust Hi-C analysis pipeline incorporating multi-mapping reads through probabilistic assignment